Note: Descriptions are shown in the official language in which they were submitted.
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DIGITAL DUPLICATE
CROSS-REFERENCE TO RELATED APPLICATIONS
[001] This application claims priority to: (1) U.S. non-provisional
application No.
16/425,886, filed on May 29, 2019 and titled "Digital Duplicate"; (2) U.S. non-
provisional
application No. 16/544,701, filed on August 19, 2019 and titled "Processes and
Systems for
Onboarding Data for a Digital Duplicate"; (3) U.S. non-provisional application
No. 16/544,702,
filed on August 19, 2019 and titled "Data Security Using Semantic Services,"
the contents of each
of which are incorporated by reference herein in their entirety.
BACKGROUND
[002] Businesses and other networks have a fundamental need to derive an
understanding
of their business/network at any moment in time, in order to engage in
strategic & operational
decision-making.
OVERVIEW
[003] Today, this need is served by a range of conventional systems for
storing,
manipulating, and accessing data. Such systems are generally limited in their
scope, flexibility,
and ability to integrate with other such systems that exist within a business
or across multiple
businesses.
[004] Part of this limitation arises from these conventional systems for
storing,
manipulating, and accessing data being built around specific business
functions. As examples,
such systems may include a CRM tool, inventory management system, accounting
system,
enterprise resource planning, payroll tool, among other examples. These
systems further suffer
from being confined to engaging in specific user functions (e.g., report
generation and
visualization, data input, etc.) that are associated with those business
functions.
[005] Further, "data warehousing" and "business intelligence" systems tend
to consume
data originating from various sources in a data network, and aggregate and pre-
process that data
to fit a predefined schema or set of dimensions. As a tool, data warehousing
is "rigid" by virtue
of the fact that the dimensions, metrics, aggregation, and delivery models
(e.g., dashboards) for
the data must be pre-defined prior to utilization. In addition, the data
contained within such
systems may also be used for the specialized simulation and modeling of
specific (narrow) areas
of the business (e.g., supply chain modeling, manufacturing planning,
financial modeling &
forecasting, etc.).
[006] Conventional systems - such as relational databases - are
advantageous for vertical
scaling (e.g., expanding a data table of 22 columns to billions of records),
but tend to be rather
limited in terms of horizontal linking and expansion across multiple tables.
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[007] In order to address these shortcomings, and to help improve upon
these and other
problems, the present disclosure seeks to reduce fixed relationships between
data tables through
the disclosed digital duplicate data structure, which utilizes a dynamic model
and method that can
be implemented thru a plurality of techniques including dynamic entity
relationships. This allows
for the digital duplicate to ingest, access data, and adapt to an
organization's changes without the
burden of redesigning the data system from the ground up, as may be required
in conventional
data structures and conventional approaches for implementing data storage
systems and data
structures.
[008] From a user standpoint, conventional data structures and conventional
approaches
for implementing data storage systems may allow for data to be accessed in
response to specific
queries as permitted by the foundational design of database structures (e.g.,
based on requirements
analysis and design, as used to design a relational database system). One
drawback to this
approach, however, is that in order to obtain a desired output from the data
storage system (e.g.,
to obtain a desired query result), the user must have a priori knowledge of
the architecture of the
data storage system, including an understanding of the data structures
utilized in the data storage
system. With the approach disclosed herein, there are no such constraints.
Indeed, the digital
duplicate may replicate the real-world physical reality of the existence of
associations between
digital records (data) describing physical assets, events and other phenomena,
and as such may be
configured to provide to users desired outputs without requiring those users
to have a priori
knowledge of the data storage architecture.
[009] In some respects, the disclosed approaches for establishing new data
structures
provide other advantages and efficiencies. As one example, relationships in
the new data
structures can be established using minimal additional logic. Further, data
ingestion occurring
from multiple data sources can, with the benefit of the present approach for
establishing new data
structures, result in data that is efficiently synthesized and arranged in the
established data
structure, helping to ensure it is consistent across an organization's entire
data store. Additionally,
once relationships between data are established, changes in any underlying
data source (e.g.,
changes to the underlying data models or structure used by the data source) do
not require
changing the established relationships.
[010] Thus, in one aspect, disclosed herein is a computer-implemented
method that
involves: establishing a data structure comprising (i) a semantic context that
has at least two data
types, and (ii) a structural context that has at least one data component,
where each component of
the structural context has associated therewith one or more respective data
properties; and
populating underlying data into an instance of the data structure such that
underlying data
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populated into each respective property of the at least one data component has
each of the at least
two data types of the semantic context.
10111 In another aspect, disclosed herein is another computer-
implemented method that
involves: retrieving a data model for a data source, establishing at least one
filter operation or at
least one clean operation that modifies an aspect of the retrieved data model,
onboarding
underlying data from the data source while applying the established at least
one filter operation or
at least one clean operation, defining at least one transformation operation
to apply to a portion of
the underlying data that has been onboarded, and applying the at least one
transformation
operation to the portion of the underlying data to thereby assign the data to
a semantic network,
the semantic network comprising conceptual data components and associative
data components.
[012] In another aspect, disclosed herein is another computer-implemented
method that
involves: receiving an indication of an instance of a semantic network, the
semantic network
comprising conceptual data components and associative data components,
receiving a selection
of one or more of the conceptual data components and associative data
components of the instance
of the semantic network, the selection comprising an indication to at least
(i) block the selected
one or more conceptual data components and associative data components or (ii)
selectively filter
the selected one or more conceptual data components and associative data
components, and
presenting a visualization of the semantic network, wherein the visualization
is configured to (i)
exclude data related to the selected one or more conceptual data components
and associative data
components or (ii) include data related to the selected one or more conceptual
data components
and associative data components and exclude data not related to the selected
one or more
conceptual data components and associative data components.
[013] In another aspect, disclosed herein is a computing system that
comprises at least
one processor, a non-transitory computer-readable medium, and program
instructions stored on
the non-transitory computer-readable medium that are executable by the at
least one processor to
cause the computing system to carry out the operations disclosed herein,
including but not limited
to the operations of the foregoing methods.
[014] In yet another aspect, disclosed herein is a non-transitory computer-
readable
medium comprising program instructions that are executable to cause a
computing system to carry
out the operations disclosed herein, including but not limited to the
operations of the foregoing
methods.
[015] One of ordinary skill in the art will appreciate these as well as
numerous other
aspects in reading the following disclosure.
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BRIEF DESCRIPTION OF THE DRAWINGS
[016] FIG. 1A depicts an example high-level functional arrangement in which
example
embodiments may be implemented.
[017] FIG. 1B depicts an example network architecture in which example
embodiments
may be implemented.
[018] FIG. 2 depicts a simplified block diagram of an example computing
device in
which example embodiments may be implemented.
[019] FIG. 3 depicts a simplified block diagram of some example data
structures
according to example embodiments.
[020] FIG. 4 is an example output produced by a device executing one
embodiment of a
software tool according to the present disclosure.
[021] FIG. 5 is a flow diagram depicting example operations that may be
carried out in
accordance with one or more embodiments of the present disclosure.
[022] FIG. 6 is a flow diagram depicting example operations that may be
carried out in
accordance with one or more embodiments of the present disclosure.
[023] FIG. 7 is a flow diagram depicting example operations that may be
carried out in
accordance with one or more embodiments of the present disclosure.
[024] FIG. 8 is an example output produced by a device executing one
embodiment of a
software tool according to the present disclosure.
[025] FIG. 9 is an example output produced by a device executing one
embodiment of a
software tool according to the present disclosure.
[026] FIG. 10 is an example output produced by a device executing one
embodiment of
a software tool according to the present disclosure.
[027] FIG. 11A is a flow diagram depicting example operations that may be
carried out
in accordance with one or more embodiments of the present disclosure.
[028] FIG. 11B is a flow diagram depicting example operations that may be
carried out
in accordance with one or more embodiments of the present disclosure.
[029] FIG. 12A is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[030] FIG. 12B is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[031] FIG. 13 is an example output produced by a device executing one
embodiment of
a software tool according to the present disclosure.
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[032] FIG. 14A is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[033] FIG. 14B is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[034] FIG. 15A is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[035] FIG. 15B is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[036] FIG. 16A is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
[037] FIG. 16B is an example output produced by a device executing one
embodiment
of a software tool according to the present disclosure.
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DETAILED DESCRIPTION
[038] The following disclosure references the accompanying figures and
several example
embodiments. One of ordinary skill in the art should understand that such
references are for the
purpose of explanation only and are therefore not meant to be limiting. Part
or all of the disclosed
systems, devices, and methods may be rearranged, combined, added to, and/or
removed in a
variety of manners, each of which is contemplated herein.
[039] The present disclosure is generally directed to technology for
implementing data
security operations in the context of semantic networks. In particular, a
"digital duplicate" may
utilize a semantic network that represents an organization's business
operations offering a unique
set of advantages over conventional systems. Specifically, by building a
digital duplicate using a
new data structure based on the neuro-synaptic model through which humans
combine and use
information in the brain, the digital duplicate may facilitate a more
efficient and dynamic means
of storing, retrieving, searching, securing, navigating, and synthesizing the
data associated with
the business or other network.
[040] When the digital duplicate is populated with the data (embodied as
digital content),
the digital duplicate may allow for the data to be contextualized in a way
that benefits from the
efficiencies realized by human cognition. Furthermore, the data may originate
from a plurality of
sources (e.g., conventional data stores or warehouses) and may be unified
and/or aggregated from
those distributed sources into the context provided by the digital duplicate.
[041] The disclosed system may be built in network-form, making large-scale
multidimensional nodes, associations, and properties of many different data
sources and types
lightweight in comparison with conventional systems. Notably, conventional
systems, such as the
semantic web, do not provide for associations to be formed automatically based
on semantic
alignment between two or more pieces of data. As disclosed, the present
architecture employs a
semantic data type, among other properties and property types, which allows
for associations to
be formed between different data from their shared semantic context,
automatically, without the
association having to be programmed into the system (as it may otherwise be in
existing systems,
such as those that utilize "triplet" form, like OWL, RDF, etc.). Accordingly,
the present disclosure
provides a technique that invention allows for rules, logic and associations
to be established and
utilized around stored data without the need for programmatic logic.
[042] In addition, the introduction of the semantic data type allows for
semantically-
identical information to be correlated even when different language is used by
different users
across a network or networks to describe that same information. This ability
to correlate
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information by its semantics enables a wealth of novel functionality relating
to data consumption,
processing, association, manipulation and use, among others.
I. EXAMPLE SYSTEM ARCHITECTURE
[043] Turning now to the figures, FIG. 1A depicts a high-level arrangement
100 of some
of the functional components that may be involved in establishing, navigating
through, and
facilitating data security operations for a digital duplicate. In one example,
three different tools
may be used to establish and navigate through various parts of a digital
duplicate 105, namely a
designer tool 102, an architect tool 103, and an organizer tool 104, among
other possible tools. At
a high level, the architect tool 103 may be used to establish what is referred
to herein as a "digital
context," which can be thought of as the framework that replicates the
language of a business.
More particularly, but still by way of example, the architect tool 103 may be
used to establish a
"semantic network" 108 that relates the terminology and conceptual meanings
behind the data
collected and stored by an organization, such as various terms, metrics, key
performance
indicators, etc. that will be used within the digital replica of the business.
As will be described
further herein, the semantic network 108 may be a dynamic network of various
data structures
that are linked together, which replaces the typical relational data model of
rows and columns
contained within disparate databases, which provides cross-functional
visibility. A semantic
network 108 may comprise nodes, links, and properties that represent core-
business elements, and
is the foundation of the digital context.
[044] A designer tool 102 may be used to introduce business logic into the
semantic
network by creating "insights" 107 that traverse the network through one or
more "pathways."
The insights 107 may then be used as a basis for information and
visualizations provided to end
users in one or more forms. The insights 107 may be created at the semantic
level, and may thus
be abstracted away from underlying source data 106.
[045] An organizer tool 104 may be used to make a connection between the
semantic
network 108 and the organization's underlying data stores 106 (which, as
depicted, may span
across multiple disparate traditional databases or other data warehouses).
This functionality may,
in some embodiments, include functionality to link multiple data sources to
the semantic network
108, as well as onboard the underlying data from the organization's underlying
data stores 106 to
the organizer data store 109 and ultimately into the semantic network 108
after filtering, cleaning,
transforming, and/or validating the data as desired. These actions may serve
to provide the system
with what is referred to as "digital content," which together with the
"digital context" form what
is referred to as a "digital duplicate."
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[046] Another tool may be used to implement data security operations for
semantic
networks referred to as an admin tool. In particular, and as described further
herein below, an
admin tool may facilitate the creation of subsets of a semantic network such
that visualizations of
the subset of the semantic network restrict or hide portions of the semantic
network not included
in the subset. Additionally, the admin tool may facilitate establishing blocks
and/or filters for a
semantic network, such that visualizations of the semantic network either
block selected portions
of the semantic network and/or only show the selected portions of the semantic
network. Such
operations may be applicable when it is desired to present visualizations of
the semantic network
to particular users but restrict from these users view certain portions of the
semantic network,
including the underlying data (i.e., the digital content) assigned to the
semantic network. As
mentioned, this functionality may be embodied in an admin tool, which may in
turn be embodied
as a software tool configured to be executed by the example system
architecture described further
herein below.
[047] Turning now to FIG. 1B, depicted herein is an example network
configuration 110
in which example embodiments of the present disclosure may be implemented. As
shown in FIG.
1B, network configuration 110 includes a back-end platform 112 that may be
communicatively
coupled to one or more client stations, depicted here, for the sake of
discussion, as client stations
113.
[048] Broadly speaking, back-end platform 112 may comprise one or more
computing
systems that have been provisioned with software for carrying out one or more
of the functions
disclosed herein, including but not limited to establishing a digital context
and ingesting data to
form a digital duplicate. The one or more computing systems of back-end
platform 112 may take
various forms and be arranged in various manners.
[049] For instance, as one possibility, back-end platform 112 may comprise
a computing
infrastructure of a public, private, and/or hybrid cloud (e.g., computing
and/or storage clusters)
that has been provisioned with software for carrying out one or more of the
functions disclosed
herein. In this respect, an entity that owns and operates back-end platform
112 may either supply
its own cloud infrastructure or may obtain the cloud infrastructure from a
third-party provider of
"on demand" computing resources, such as Amazon Web Services (AWS) or the
like. As another
possibility, back-end platform 112 may comprise one or more dedicated servers
that have been
provisioned with software for carrying out one or more of the functions
disclosed herein. Other
implementations of back-end platform 112 are possible as well.
[050] In turn, client stations 113 may each be any computing device that is
capable of
running the front-end software disclosed herein. In this respect, client
stations 113 may each
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include hardware components such as a processor, data storage, a user
interface, and a network
interface, among others, as well as software components that facilitate the
client station's ability
to run the front-end software disclosed herein (e.g., operating system
software, web browser
software, etc.). As representative examples, client stations 113 may each take
the form of a desktop
computer, a laptop, a netbook, a tablet, a smartphone, and/or a personal
digital assistant (PDA),
among other possibilities.
[051] As further depicted in FIG. 1B, back-end platform 112 is configured
to interact
with client stations 113 over respective communication paths 111. In this
respect, each
communication path 111 between back-end platform 112 and one of client
stations 113 may
generally comprise one or more communication networks and/or communications
links, which
may take any of various forms. For instance, each respective communication
path 111 with back-
end platform 112 may include any one or more of point-to-point links, Personal
Area Networks
(PANs), Local-Area Networks (LANs), Wide-Area Networks (WANs) such as the
Internet or
cellular networks, cloud networks, and/or operational technology (OT)
networks, among other
possibilities. Further, the communication networks and/or links that make up
each respective
communication path 111 with back-end platform 112 may be wireless, wired, or
some
combination thereof, and may carry data according to any of various different
communication
protocols. Although not shown, the respective communication paths 111 between
client stations
113 and back-end platform 112 may also include one or more intermediate
systems. For example,
it is possible that back-end platform 112 may communicate with a given client
station 113 via one
or more intermediary systems, such as a host server (not shown). Many other
configurations are
also possible.
[052] The interaction between client stations 113 and back-end platform 112
may take
various forms. As one possibility, client stations 113 may send certain user
input related to a digital
duplicate to back-end platform 112, which may in turn trigger back-end
platform 112 to take one
or more actions based on the user input. As another possibility, client
stations 113 may send a
request to back-end platform 112 for certain data and/or a certain front-end
software module, and
client stations 113 may then receive digital duplicate data (and perhaps
related instructions) from
back-end platform 112 in response to such a request. As yet another
possibility, back-end platform
112 may be configured to "push" certain types of digital duplicate data to
client stations 113, in
which case client stations 113 may receive digital duplicate data (and perhaps
related instructions)
from back-end platform 112 in this manner. As still another possibility, back-
end platform 112
may be configured to make certain types of digital duplicate data available
via an API, a service,
or the like, in which case client stations 113 may receive data from back-end
platform 112 by
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accessing such an API or subscribing to such a service. The interaction
between client stations
113 and back-end platform 112 may take various other forms as well.
[053] As also shown in FIG. 1B, back-end platform 112 may also be
configured to
communicate with one or more data sources 114, such as external databases,
internal databases,
and/or another back-end platform or platforms. Such data sources ¨ and the
data output by such
data sources ¨ may take various forms. Further, back-end platform 112 and the
one or more
external data sources 114 may be configured to interact over a communication
path 111, which
may take the form or forms discussed above with respect to the other
communication paths 111.
[054] It should be understood that network configuration 110 is one example
of a
network configuration in which embodiments described herein may be
implemented. Numerous
other arrangements are possible and contemplated herein. For instance, other
network
configurations may include additional components not pictured and/or more or
less of the pictured
components.
EXAMPLE COMPUTING DEVICE
[055] FIG. 2 is a simplified block diagram illustrating some structural
components that
may be included in an example computing device 200, which could serve as, for
instance, the
back-end platform 112 and/or one or more of client stations 113 in FIG. 1B. In
line with the
discussion above, computing device 200 may generally include at least a
processor 202, data
storage 204, and a communication interface 206, all of which may be
communicatively linked by
a communication link 208 that may take the form of a system bus or some other
connection
mechanism.
[056] Processor 202 may comprise one or more processor components, such as
general-
purpose processors (e.g., a single- or multi-core microprocessor), special-
purpose processors (e.g.,
an application-specific integrated circuit or digital-signal processor),
programmable logic devices
(e.g., a field programmable gate array), controllers (e.g., microcontrollers),
and/or any other
processor components now known or later developed. In line with the discussion
above, it should
also be understood that processor 202 could comprise processing components
that are distributed
across a plurality of physical computing devices connected via a network, such
as a computing
cluster of a public, private, or hybrid cloud.
[057] In turn, data storage 204 may comprise one or more non-transitory
computer-
readable storage mediums, examples of which may include volatile storage
mediums such as
random-access memory, registers, cache, etc. and non-volatile storage mediums
such as read-only
memory, a hard-disk drive, a solid-state drive, flash memory, an optical-
storage device, etc. In
line with the discussion above, it should also be understood that data storage
204 may comprise
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computer-readable storage mediums that are distributed across a plurality of
physical computing
devices connected via a network, such as a storage cluster of a public,
private, or hybrid cloud.
[058] As shown in FIG. 2, data storage 204 may be provisioned with software
components that enable the computing device 200 to carry out the operations
disclosed herein.
These software components may generally take the form of program instructions
that are
executable by the processor 202 to carry out the disclosed functions, which
may be arranged
together into software applications, virtual machines, software development
kits, toolsets, or the
like, all of which are referred to herein as a software tool or software
tools. Further, data storage
204 may be arranged to store data in one or more databases, file systems, or
the like. Data storage
204 may take other forms and/or store data in other manners as well.
[059] Communication interface 206 may be configured to facilitate wireless
and/or wired
communication with other computing devices or systems, such as one or more
client stations 113
when computing device 200 serves as back-end platform 112, or as back-end
platform 112 when
computing device 200 serves as one of client stations 113. As such,
communication interface 206
may take any suitable form for carrying out these functions, examples of which
may include an
Ethernet interface, a serial bus interface (e.g., Firewire, USB 3.0, etc.), a
chipset and antenna
adapted to facilitate wireless communication, and/or any other interface that
provides for wireless
and/or wired communication. Communication interface 206 may also include
multiple
communication interfaces of different types. Other configurations are possible
as well.
[060] Although not shown, computing device 200 may additionally include one
or more
other interfaces that provide connectivity with external user-interface
equipment (sometimes
referred to as "peripherals"), such as a keyboard, a mouse or trackpad, a
display screen, a touch-
sensitive interface, a stylus, a virtual-reality headset, speakers, etc.,
which may allow for direct
user interaction with computing device 200.
[061] It should be understood that computing device 200 is one example of a
computing
device that may be used with the embodiments described herein. Numerous other
arrangements
are possible and contemplated herein. For instance, other computing devices
may include
additional components not pictured and/or more or fewer of the pictured
components.
III. DIGITAL DUPLICATE DATA STRUCTURES
[062] As mentioned, the present disclosure is directed to a new approach
for structuring
an organization's, a business's, or a network's data as well as processes for
implementing data
security operations within this approach, all of which may help to facilitate
more efficient access
to this data. At a high level, this approach involves establishing a digital
context and populating
the digital context with digital content to thereby form what is referred to
herein as a digital
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duplicate. Deploying a digital duplicate in practice includes the high-level
steps of first creating
the digital context, and second adding data to this digital context. The
digital duplicate may be
kept live or refreshed repeatedly over time by continuously updating the
digital context as the
organization's, business's, or network's data changes and the digital content
as the data and the
data sources change. While elements of the digital context and digital content
may change, the
core data structure of the digital duplicate does not typically change,
allowing the information to
remain consistent without having to change the design of the data structure.
[063] FIG. 3 is a simplified block diagram, illustrating an example digital
duplicate data
structure architecture 300 according to an example embodiment of the present
disclosure. At a
high level, and as depicted, digital duplicate data structures 300 may include
a digital context 310
and digital content 320, which together form what is referred to herein as an
instance of a digital
duplicate 301. The data structures 300 also include a registry 302 and a data
store 303. These
various data structures are described herein in further detail.
A. Digital Context
[064] At a more specific level, but still by way of example, FIG. 3 depicts
an example
architecture diagram illustrating certain data structures included within
digital context 310. As
mentioned, digital context 310 is a data structure that generally comprises a
network of individual
data components. This network of data components may include structural
context components
and semantic context components. These components may be stored in data store
as will be
described further herein.
[065] Turning first to the structural context components, these structural
context
components may generally describe how the data is structured and stored in the
digital context. In
one implementation, the structural context components may include conceptual
components 314
(sometimes referred to herein as concepts) and associative components 316
(sometimes referred
to herein as associations). And these components may have one or more
respective properties 315,
317. These components may be designed to hold data that describes various
aspects about how an
organization's information is structured within the digital duplicate 301 as
well as how this
information relates to itself. Although these components are depicted as
blocks in a simplified
block diagram, it should be understood that the underlying data represented by
these blocks may
be stored in an appropriate storage location of data store 303, which may at
time be referred to
herein as a directory.
[066] A conceptual component 314 may generally be a data structure that is
designed to
hold data that describes one aspect of an organization's business. To
illustrate with an example
for a particular organization in the medical services industry, one example
conceptual component
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may be a "physician" component where this conceptual component may be designed
to hold data
that describes the physicians that are employed by the particular
organization. To this end, the
"physician" conceptual component may include various properties 315 for
holding such data,
including a "Last Name" property, a "First Name" property, a "Specialty"
property, a "Telephone
Number" property, and/or a "Years in Service" property, among other examples.
[067] In some cases, properties may be shared across multiple conceptual
components.
For example, the "specialty" property may be shared across multiple
"Physician" conceptual
components and/or the "Clinic" conceptual component. In situations in which a
property is widely
shared across multiple conceptual components, the digital context may be
configured to promote
the "specialty" property from a property to a separate concept. This may be
accomplished without
changing the underlying data structure but rather reconfiguring it. This
ability of the neuro-
semantic network to adapt and learn as the organization changes makes it a
scalable and learning
model. The method provides for the ability to promote properties into concepts
or to collapse them
into concepts and associations to best represent the current structure of the
organization.
[068] Another example conceptual component 314 may be a "patient" component
where
this conceptual component may be designed to hold data that describes the
individuals that are
patients of the various physicians who are employed by the particular
organization. To this end,
the "patient" conceptual component may include various properties 315 for
holding such data,
including a "Last Name" property, a "First Name" property, a "Home Address"
property, and/or
a "preferred Payment Method" property, among other examples.
[069] Yet another example conceptual component 314 may be a "clinic"
component
where this conceptual component may be designed to hold data that describes
the various clinical
facilities utilized by the particular organization. To this end, the "clinic"
conceptual component
may include various properties 315 for holding such data, including a "Clinic
Name" property, an
"Address" property, a "Services Offered" property, and/or a "Capacity"
property, among other
examples.
[070] As depicted, another type of structural component of the digital
context may be an
associative component 316. An associative component is similar to a structural
component in that
it is designed to hold data that describes one aspect of an organization's
business. But more
specifically, the associative component is also designed to hold data that (i)
describes an aspect of
the organization's business such as an activity or a metric and (ii) relates
together to two or more
conceptual components 314. As an example, one example associative component
for the particular
organization in the medical services industry may be a "visit" component
designed to hold data
that describes a particular patient's visit to a particular physician at a
particular clinic and is thus
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associative of multiple conceptual components, including the example
"physician," "patient," and
"clinic" structural components described above. To this end, the "visit"
associative component
may include various properties 317, including a "Date of Visit" property, a
"Duration of Visit"
property, "Billed Value of Visit," and/or a "Diagnosis of Visit" property,
among other examples.
[071] As mentioned throughout the examples given above, structural context
components, including both conceptual components and associative components,
include various
properties 315, 317 for holding certain specific descriptive data for the
structural context
component. In some implementations, each individual property of a given
structural context
component may be described by a particular combination of a structural data
type 318 and a
semantic data type 313, which may thus form a semantic component.
[072] Generally, a structural data type 318 applied to information is data
that describes
how the information is stored within the system. Many different structural
data types are possible.
As one example, a structural data type may take the form of a "temporal" data
type, under which
a "Years in Service" property may fall. As another example, a structural data
type may take the
form of a "spatial" data type, under which a "Clinic Address" property may
fall. As another
example, a structural data type may take the form of a "physical" data type,
under which a "Clinic"
and the "Clinic Name" property may fall. As another example, a structural data
type may take the
form of a "Personal" data type, under which a "Last Name" data type may fall.
As another
example, a structural data type may take the form of a "Quantitative" data
type, under which a
"Billed Value of Visit" property may fall. As another example, a structural
data type may take the
form of a "Categorical" data type, under which a "Specialty" property may
fall. It should be
appreciated that other examples may be possible as well.
[073] Generally, a structural data type helps define how data is managed,
indexed, and
stored for all similar properties in the network. Properties with common
structural data type may
use common data structures to store and retrieve data across a digital
duplicate and provide an
efficient way to store, access and relate data; allowing for unique
computations; and provide better
methods to access, resolve and compare similar data. For example, all
"temporal" data types may
share or "index" to a common timeline data structure that allows independent
events like a sale
event and a marketing discount that happened during the same month without
having to explicitly
compare data. This provides an ability to not only perform unique computations
and analysis on
properties with similar structural data like "same month," or "same quarter,"
but also compare
financial results of two unrelated companies for the same quarter even though
they belong to
different business networks because they use the same temporal data type. In
another case, if two
separate networks provide the population and economic data for the same
spatial data type (such
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as a zip code), it allows one to overlay and contrast population and GDP for
the same zip code
with minimal effort. Multiple similar storage and advantages can be added to
across all shared
structural data types by creating a shared structural data type and storage
model across properties
in a network.
[074] Structural data types like "temporal," "spatial," "personal," or
"organizational"
may allow data and methods to be shared across one or more properties in a
network or across
whole networks using a common data structure like a shared timeline, time
resolution, or temporal
methods; while semantic data types (discussed below) allows for data and
methods to be shared
across a network using common meaning. Shared structural data types may also
have shared
resolution and absolute values. For instance, "February 2015" will have a
resolution of 1 day and
may be a delivery date to a customer or the start date of an employee. This
allows shared
computations like "Start Month" or "Delivery Month" to be performed.
[075] As also indicated, each property may also have a semantic data type
313.
Generally, a semantic data type applied to underlying information is data that
describes what the
information means. A semantic data type may have various aspects that
facilitate describing what
the information means. One aspect that a semantic data type may have is called
a primitive data
type. A primitive data type may describe the general form of the information.
Example primitive
data types may include "integer," "Boolean," "string," "float," etc. Another
aspect that a semantic
data type may have is a pointer that points to a particular function that may
be associated with the
information. This pointer may be stored in the dictionary entry 312 for the
particular semantic
data type and may point to various kinds functionality. As one example, the
pointer may point to
a web method for utilizing the underlying information. A web method may be any
operation or
set of operations embodied in a web service, API, or the like. For instance,
one web method may
be a "mailto:EmailAddress" web method that refers to a web method that causes
an email client
to be invoked, generate a new email message, and populate the "To:" field with
the email address
represented by the data variable "EmailAddress." Other web methods are
possible as well.
[076] Another example of a function to which a pointer may point is
mathematical
operation performed using the underlying information represented by the
semantic data type. For
instance, one type of mathematical operation for a "date of birth" semantic
data type may be an
age computation function. With such a function, the system may compute the age
of an individual
represented by the underlying date of birth information by, for instance
subtracting the "date of
birth" date from "current date" data to arrive at "age" data.
[077] Another type of mathematical function for a "price per unit" semantic
data type
may be a total price aggregation function. With such a function, the system
may aggregate all of
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the data values from various "price per unit" data types to arrive a total
price value. Such a function
may be useful in situations where a customer is purchasing products or
services in a single order
that stems from two or more aspects of a business, which may not have
aggregated their data
systems in advance. Applying the "price per unit" semantic data type (or, in
other examples, a
similar-functioning semantic data type) serves to link the pricing across what
may be disparate
aspects of the organization and/or disparate data systems.
[078] Another type of mathematical function for a "lead time" semantic data
type may
be a lead time aggregation function. A "lead time" semantic data type may be
associated with a
product, component of a product, subassembly, construction project, etc. With
such a function,
when a customer purchases multiple products at once, an aggregation function
may be executed
in which the system may automatically populate "lead time" data by selecting
the individual lead
time field for each of the purchased products that has the greatest lead time
value. In cases in
which a product may not have a lead time associated with it, the lead time of
each subassembly or
component that makes up the product may by summed to approximate the total
lead time of the
product.
[079] In one example, during data ingestion, the system may capture various
data fields
for an order, including a "deliveryDate" field for describing the delivery
date of an order, an
"orderDate" field for describing the date of the placement of the order, and a
"deliveryTime" field
for describing the time taken for the order to be fulfilled after the product
is fully manufactured
and stocked in inventory, all of which may be specified by various a logistics
or fulfillment
systems. At this stage, the system may compute the actual lead time of the
product to be the
function of (deliveryDate - orderDate) - deliveryTime. Therefore, in the case
where a product is
not built before it is ordered (as is common in the heavy equipment industry,
for example) lead
time may be a residual value, as calculated above. Once lead time is known,
the system may then
engage in a function that compares the actual lead time with the approximated
lead time, which
may be made possible by the existence the "lead time" semantic data type being
used across
multiple business systems that is semantically distinct from a "delivery time"
type. A further
function may add an "error" to the function for computation of approximated
lead times for all
other products, which in turn may propagate the new calculation of
approximated lead times
throughout the digital duplicate instantaneously. In this way, the system may
engage in a kind of
machine learning.
[080] Another example of a function to which a pointer may point is a
linking function
that may operate to link two or more semantic data types together and form a
new property of an
associative structural component. As one example of this, a distance function
may link together
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an "address" property of a "patient" conceptual component and an "address"
property of a "clinic"
conceptual component and computes the distance between these two addresses.
The function may
then save this distance as a new property of a new associative component.
[081] Yet another example of a function to which a pointer may point is a
semantic search
function. With such a function, a search may be executed on a given semantic
data type, which
may retrieve data of the same semantic type from other areas of the
organization or other network.
[082] To help illustrate, consider an example in which respective digital
duplicates have
been established for different aspects of an organization. Each such digital
duplicate will have its
own set of data components stored separately from the data components of the
other digital
duplicates. In a situation in which a user desires to know all employees that
share duties or interact
across the organization's departments, a semantic query can be issued on an
"Employee" semantic
data type. In the context of the present disclosure, such a semantic search
may return all data
objects that are based on this semantic type, regardless of the content,
format, or location of the
data. In this way, the semantic search unifies various disconnected digital
representations. With
conventional approaches, by contrast, a typical search would fail here,
because the data may be
spread out across multiple different databases and arranged in multiple
tables; and as such, any
query would need to account for these multiple databases and the various
tables.
[083] Considering another example, say a user desires to know all entities
(e.g., dealers,
customers, vendors, employees, etc.) having a specific area code. In the
context of the present
disclosure, the user could issue a single query on a "Phone No." data type for
the specific area
code of interest. Such a query would return all data objects having the
specific area code of interest
no matter the location or format of the data. By contrast, with a conventional
approach, a user may
need a deep understanding of the organization's data storage structure in
order to carry out this
query. For instance, the user may need to know what table the employee records
are stored in and
what field and what format the phone number data is stored in. Likewise, the
user may need to
know this same information for the dealer records, the customer records, the
vendor records, etc.
Each additional storage location may add complexity to the query. And to the
extent that the data
is stored in disparate data stores (such as one data warehouse for employee
records and another
data warehouse for vendor records), then the user may need to issue separate
queries for each such
disparate data source further compounding the complexity and vulnerability for
user error. Thus,
with the benefit of the present disclosure, it should be understood how the
semantic data type
provides for more efficient data retrieval, among other advantages.
[084] In some embodiments, user interface elements presented by one or more
computing
devices disclosed herein (e.g., client stations 113) may reflect semantic data
types with specific
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graphical elements, such as icons. As one example, on a user interface that is
displaying multiple
semantic data types for an organization, the user interface may display a
telephone icon adjacent
to data that is of a "phone number" semantic data type, and/or a map icon if
the data is of an
"address" semantic data type, although other examples are possible. It should
be understood that
the functions disclosed herein are merely examples, and that in other
implementations, other
functions may be possible as well.
[085] Depending on the organization, semantic data types can be arranged
into various
semantic groups. A semantic group is generally a set of one or more semantic
data types that are
relevant to a particular categorical aspect of the organization. For instance,
example semantic
groups for an organization may be "Financial & Accounting," "Production &
Manufacturing,"
"Purchasing," and/or "Logistics." In this way, an organization may arrange the
semantic data
types into groups that are reflective of the organization's operating
departments or sectors. Thus,
the "Financial & Accounting," semantic group may have semantic data types that
refer to aspects
of the organization's own financial & accounting department, the "Production &
Manufacturing,"
semantic group may have semantic data types that refer to the aspects of the
organization's own
production and manufacturing operations, etc. As such, these semantic data
types may more
accurately describe the organization's own business operations and may thus be
more useful to
the organization.
[086] Semantic data types may provide various advantages to organizations
who utilize
the digital duplicate schema set forth in FIG. 3 and generally described
herein. As one advantage,
the semantic data type 313 may serve to discriminate between (i) human
language used to describe
an aspect of the organization's operations (which can be stored as the name of
a property, in one
embodiment) and (ii) the underlying meaning of the human language used to
describe the aspect
of the organization's operations (which can be stored as the semantic data
type, in one
embodiment). This discrimination may thus allow for properties in the digital
duplicate to be
unified by their underlying meaning (i.e., unified by their semantic data
type) even when the
human language used to describe them (i.e., their property names) may not be
the same.
[087] More particularly, but still by way of example, the digital duplicate
architecture
300 encourages this unification by having data sets that are consistently
accurate and complete
because no data is mismatched within a given context. To illustrate, if one
property is called
"Digits," and another property is called "Phone No." but these properties
refer to the same thing,
they both may be pulled into a report, a visualization, a computation, or used
in some other way
by the computing system when the digital duplicate calls for the semantic data
type 'Telephone
Number' within a given context. This may be accomplished through an
arrangement where
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"Telephone Number" is a semantic data type that is shared by both the "Digits"
and "Phone No."
properties. In this way, the semantic data type may be said to "unify" two (or
more) properties by
these properties' underlying meanings.
[088] Unification may also allow for functions to be associated with
different properties
of the same semantic data type. To illustrate, as indicated above, "Digits"
and "Phone No." may
be two different properties that share the same semantic data type "Telephone
Number."
Therefore, both "Digits" and "Phone No." may have a pointer that points to a
"Make-Call"
function, which is assigned to these properties by virtue of their shared
semantic data type.
[089] Unification may also occur by enriching the structural context of the
digital
duplicate as a result of automating through-computation of additional
properties based on the
semantic data type(s) of the original properties and the functions available
for the semantic data
type(s). To illustrate using the example from above, the function for
computing "Age" from the
"Date of Birth" semantic data type is a form of unification because "Birth
Date" and "Date of
Birth" may be distinct properties in the digital duplicate but share the same
semantic data type:
"Date of Birth." Other examples of how the digital duplicate architecture
results in unification
are possible as well. The combination of the concept (node) or association
(link) that describes a
property in combination with a semantic data type (and in many cases a
structural data type)
individually and combined also create a strong representation of the digital
context. When
combined, they provide not only a simple way to locate every piece of data in
the business
network, and to locate a relative position of the data to other pieces of data
for navigation and
comparison, but also may provide meaning to the data and structure for
storage. Once combined,
these data structures create ways to simply and efficiently create, manage,
and navigate data in a
business or network using the digital context.
[090] As also depicted in FIG. 3, digital context 310 may contain a
composite structure
319. A composite structure 319 may contain one or more indications of sets of
concepts and
associations that represent various aspects of an organization. One type of
composite structure
may be a layer of concepts and associations. The concepts and associations
that comprise a layer
may represent similar aspects of the organization. In one example, an
organization in the medical
services industry may have a "pharmaceutical" layer that comprises concepts
and associations
related to any pharmaceutical aspects of the organization, such as pharmacy
employees, pharmacy
inventories, and/or an employee layer that comprises concepts and associations
related to
employees across all departments. Another type of composite structure may be a
realm of concepts
and associations. The concepts and associations that comprise a realm may
represent aspects of
the organization that are grouped on a broader level. For instance, a large
organization that is made
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up of or owns several smaller businesses may have a realm that comprises all
the concepts and
associations for one entire business and a realm that comprises all the
concepts and associations
for another entire business. Yet another type of composite structure may be an
insight. The
concepts and associations that comprise an insight may represent collections
of concepts and
insights that have been automatically identified by the system as having some
threshold number
of relationships. The system may identify such insights when certain patterns
develop in the
organization's digital context (e.g., a threshold number of associations
between various concepts,
and/or a threshold number of shared properties between multiple concepts or
associations), and in
this may be identify to users unique aspects of the organization's operations.
Other examples of
layers, realms, and insights are possible as are other types of composite
structures.
B. Digital Content
[091] As also depicted in FIG. 3, the digital duplicate 301 includes
digital content 320.
Generally, digital content 320 is the underlying data that populates one or
more instances of the
framework for the digital duplicate (i.e., the digital context 310) described
above. Such digital
content may comprise data that may be ingested into the system (in accordance
with, perhaps, the
functionality associated with the organizer software tool described further
herein below) from one
or more data sources, such as business systems (e.g. CRM systems, ERP systems,
POS systems,
accounting software, etc.), enterprise data stores, data warehouses, data
lakes, operational data
stores, as well as any other type of kind of databases or data store, such as
data inputted by a user,
data mined from research reports, among other examples.
[092] This underlying data may be either static data, data updated in a
batched manner,
such as on a periodic or aperiodic refresh cycle, or data updated in real-time
or near real-time (e.g.,
data provided to the system in the form of a data "stream", which may or may
not be buffered to
align with the update frequency of the Digital Duplicate's data ingestion
method). Other examples
of data ingestion may be possible as well.
[093] As depicted, digital content may generally be comprised of links and
nodes. In
particular digital content 320 may include node data 321, node properties 322,
and node instances
323. Further, digital content 320 may also include link data 325, link
properties 326, and link
instances 327.
[094] As a general matter, node data 321 and link data 325 may include
underlying
instances of an organization's information that populates a digital context
schema, examples of
which have been described above. Node data 321 in particular may include the
underlying
information that populates the conceptual components of the digital context.
Referring back to the
examples described above, one example conceptual component may be a
"physician" component
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where this conceptual component may be designed to hold data that describes
the physicians that
are employed by a particular medical services organization. Node data 321 may
thus include
underlying organization information for the "physician" component, such as
individual instances
323 of particular physician information. Thus, for each instance of
information that populates the
"physician" conceptual component, node data 321 may include a corresponding
node. The
underlying information within each respective node may be arranged into node
properties 322 in
accordance with the property structure defined by the conceptual component.
For instance, one
"physician" node may include node property data "Smith" for the "Last Name"
property of the
conceptual component, "John" for the "First Name" property of the conceptual
component,
"Pediatrics" for the "Specialty" property of the conceptual component, "555-
867-5309" for the
"Telephone Number" property of the conceptual component, and "30" for the
"Years in Service"
property of the conceptual component, although other examples are possible.
[095] Similarly, link data 325 may include the underlying information that
populates the
associative components of the digital context. Referring back to the examples
described above,
one example associative component may be a "visit" component where this
associative component
may be designed to hold data that describes a particular patient's visit to a
particular physician at
a particular clinic. Link data 325 may thus include underlying organization
information for the
"visit" component, such as individual instances 327 of particular visit
information. Thus, for each
instance of information that populates the "visit" associative component, link
data 325 may
include a corresponding link. The underlying information within each
respective link may be
arranged into link properties 326 in accordance with the property structure
defined by the
associative component. For instance, one "visit" link may include link
property data "Jan. 2, 2020"
for the "Visit Date" property of the associative component, "1 hour" for the
"Duration of Visit"
property of the associative component, and "$110" for the "Billed Value"
property of the
conceptual component, although other examples are possible.
C. Storage Schema
[096] The network of individual data components described above may be
stored in one
or more data stores 303 in various ways. The structure of the digital context
and well as the storage
schema, as described herein, allows for network traversal as well as semantic
searches (described
above) while querying for data. As a result of traversal of the "data
network," subsets of the data
can be efficiently retrieved and presented to one or more users. Data stores
303 may take the form
of one or more of SQL Server, Oracle, Mongo DB, or other storage technologies.
[097] As one example of the various ways in which the individual data
components may
be stored in data stores 303, relationships between conceptual components 314
and associative
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components 316 may be stored using what are referred to as unique identifiers
("UIDs"). In this
way, each element of each instance of the digital duplicate 301 may have an
associated UID
(which, depending on the situation, may or may not be unique). As outlined
above, the various
elements that may have a UID assigned thereto may be (i) domains, (ii)
subdomains, (iii)
directories, (iv) conceptual components, (v) associative components, (vi)
properties, (vii)
dictionaries, (viii) semantic groups, and/or (ix) semantic data types. In some
implementation, a
UID may take the form of a URI (Uniform Resource Identifier), or any other
standard identifier
type, among other examples.
[098] As an illustrative example the "Patient" conceptual component may
exist in data
storage 303 in, for instance, table form with underlying digital data
populated for the component
in the form of [P1, P2, P3, etc.]. Likewise, the "Physicians" conceptual
component may exist in
data storage 303 in, for instance, table form with underlying digital data
populated for the
component in the form of [H1, H2, H3, etc.]. Likewise, the "Clinics"
conceptual component may
exist in data storage in table form with underlying digital data populated for
the component in the
form of [C1, C2, C3, etc.].
[099] Using this arrangement, the "Visits" associative component may
accordingly exist
in data storage 303 in, for instance, table form with underlying digital data
populated for the
associative component in the form of a table containing intersecting data from
the other related
conceptual components. As an example, one specific instance of the "Visit"
component may have
data that takes the form [P1, H3, C2], where this instance describes a visit
that took place by
patient "P1" who was treated by physician "H3" at clinic "C3," although other
combinations are
be possible.
[100] Reciprocal data tables may be stored in data storage 303 as well. A
reciprocal table
may serve to identify, for the conceptual component data, whether and to what
extent there is
associative component data that relates in some way to the conceptual
component data. Using the
examples set forth above, the "Patient" conceptual component discussed above
may contain a
reciprocal table that intersects its dependent structural components for each
instance of a "Patient,"
where one instance for Patient "P1" may take the form of [V1, H3, C2]. Other
examples of
reciprocal tables may be possible as well.
[101] In this way, the data defining the schema for the digital duplicate
may be embodied
as a "data network" or form of neurosynaptic storage of information, where
associative
information (such as that described above) is stored at the intersection point
of the structural
components. Each instance of such data tables for corresponding "Visits,"
"Patients,"
"Physicians," and "Clinics" (as examples) may be created from source data by
an organizer part
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of the data ingestion method, described below. This provides certain
advantages over traditional
data storage models, such as relational models that use fixed relationships
between data. As one
advantage, the present technique uses a single, atomic template to implement
each structural
association and its corresponding components in the appropriate storage model.
As such, this
technique allows for dynamic expansion to create as many associations as may
be desired to
represent the desired comprehensive network. In comparison to NoSQL databases
that store
entities as collection of key-value pairs and allow for each record to have a
variable structure in
each table, or graph databases that use key-value pairs to store relationships
between two values,
the digital duplicate architecture allows information to be stored within a
flexible neurosynaptic
data structure to describe the data using the directory, dictionary, and the
structural data types.
This provides dramatic flexibility both to store and locate data accurately
and to capture additional
business information within the network.
[102] Further, the data defining the schema for the digital duplicate can
be stored in data
storage 303 via tables that represent all relationships that comprise the
network of components
(referred to herein as the "Digital Context" 301). And in this way, data
ingested can be placed
within this digital context 301. In some implementations, this technique may
provide for
traceability between data sources and its corresponding data context using
UIDs for each source
of data and for each contextual element. As an example of this, a patient's
"First Name" data
element may reference the UID of the structural elements corresponding to this
data element (for
example, the patient's associated visits) and vice-versa.
[103] The system may be further configured to store a particular digital
context 310
and/or the underlying digital content 320 for the particular digital context
with an indication that
the particular digital context and/or the underlying digital content belongs
to unique domain 311
or subdomain. For instance, a unique domain (and/or subdomain) may be
established for each
instance of the digital duplicate and may be stored in a registry 302. A
registry 302 may contain
(a) a list of domains and (b) a list of all subdomains that exist within each
domain. For instance,
returning to the example organization in the medical services industry, the
list of domains may
contain a domain indicator (e.g., a URI) that is specific to this
organization. The domain indicator
may thus represent all the data that is stored as the digital content for a
digital duplicate related to
this organization. Within each domain, there may be one or more subdomains for
individual data
contexts for the organization. For instance, within the domain for the example
organization, in the
medical services industry, there may be a subdomain for "Purchasing," and a
subdomain for
"Marketing," among other examples. This, the list of subdomains may contain
subdomain
indicators (e.g., URIs) that identify these subdomains.
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[104] A registry 302 may also contain data describing locations and
identifiers of
authentication security services for users accessing data within a given
domain. For instance,
domains and subdomains may be private (accessible only to users within an
organization), and as
such may contain such authentication data that serves to describes the various
user that have
appropriate permissions to access the given domains and/or subdomains. Domains
and
subdomains may also be public, and therefore accessible to any users or
systems outside of an
organization. Other examples of data that may be stored in the registry are
possible as well.
[105] As explained, the schema for one instance of a digital context may be
stored in or
with what is referred to as a "dictionary" 312. In this way, a single
dictionary 312 may store data
that describes the digital context 310 for one specific organization. The
system may thus store
multiple dictionaries, with one dictionary being stored for each specific
organization that utilizes
the system to create and store an instance of a digital duplicate 301. In some
implementations,
however, dictionaries may be shared between domains and/or subdomains. For
instance, if a first
organization in the medical services industry has already established a
dictionary that stores its
schema data describing its digital context, then a second similar medial
services organization may
benefit from using this same dictionary already established for the first
organization. In this way,
a common set of semantics may be used across organizations in the same or
similar industries.
[106] The digital duplicate may be stored via data store 303 using any
appropriate data
storage technology, including by way of example, graphical databases,
relational databases (SQL,
Oracle), in-memory data storage, as well as other types of storage. Digital
duplicate information
may be stored in two or more such database technologies for redundancy and/or
performance
purposes.
[107] In some implementations an index file may be used as a separation of
concerns
measure. For instance, an index file that may contain data keys may reside in
one location and the
digital duplicate data may reside in another, perhaps remote, location. In
this way, a set of semantic
services may be employed to store and retrieve data specific to the underlying
digital duplicate
data by first accessing the data keys and then using those data keys to
identify and access the
location of the underlying digital duplicate data.
IV. EXAMPLE VISUALIZATIONS
[108] A computing device, such as computing device 200 (FIG. 2), which as
described
above, may serve as one or more of client stations 113 (FIG. 1B) and/or back-
end platform 112
(FIG. 1B), may be configured to generate various visualizations of an
established digital context.
In one example, a computing device may be configured to generate a
visualization of an entire
digital context (which as mentioned above may, from time to time, be referred
to herein as a
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"semantic network") established for a particular organization, where the
structural components of
the semantic network are represented as "nodes" and the associative components
of the semantic
network are represented as "links" between the nodes thus forming a web-like
structure.
[109] To illustrate one example of this, FIG. 4 depicts an example snapshot
400 of a
graphical user interface (GUI) that provides a web visualization 402 and a
listing 404 of various
nodes and links that may comprise an example semantic network. As depicted,
the web
visualization 402 includes a number of nodes, such as example node 406 for
"Product" and
example node 410 for "Ship Date." As explained with reference to a different
example above, the
"Product" node 406 may include various properties that contain data for
describing a product,
such as product name, product model number, etc. Likewise, "Ship Date" node
410 may include
various properties that contain data for describing various shipping dates
whereon various
products were shipped to customers of the organization or the like, such as
month, day, year, etc.
As also depicted, web visualization 402 also includes a number of links, such
as "Orders" link
408, which may contain data that relates together data represented by two or
more nodes. The
"Orders" link 408, for instance, may contain data that describes various
instances of customer
orders for particular products.
[110] A computing device may take one or more actions responsive to
receiving user
inputs via a GUI that displays a visualization, such as the example
visualization depicted in FIG.
4. For instance, responsive to receiving a selection of one of the nodes or
links displayed in web
visualization 402 (e.g., through a mouse click or a tap on a touch-screen
interface, or the like), the
computing device may display further information related to the selected node
or link, such as
displaying the properties of the selected node or link, or displaying data
related to the underlying
data records that have been assigned to the selected node or link, where such
data may be in the
form of a data table, graph, or some other format outlining or summarizing the
underlying data
records. The computing device may take other actions responsive to receiving
various user input
via the GUI as well, such as by rearranging or repositioning the various nodes
and links of the
semantic network in response to receiving a click-and-drag input via a mouse
or a touch-and-drag
input via a touch screen, among other possible inputs and responsive actions.
V. EXAMPLE OPERATIONS
A. Example Digital Context and Digital Content Creation Operations
[111] Example operations that may be carried out by one or more computing
devices
running the disclosed software tool are discussed in detail below. For
purposes of illustration only,
these example operations are described as being carried out by a computing
device, such as
computing device 200 (FIG. 2), which as described above, may serve as one or
more of client
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stations 112 (FIG. 1) and/or back-end platform 102 (FIG. 1). In this respect,
it should be
understood that, depending on the implementation, the operations discussed
herein below may be
carried out entirely by a single computing device, such as one or more of
client stations 112 or by
back-end platform 102, or may be carried out by a combination of computing
devices, with some
operations being carried out by back-end platform 102 (such as computational
processes and data-
access operations) and other operations being carried out by one or more of
client stations 112
(such as display operations and operations that receive user inputs). However,
other arrangements
are possible as well.
[112] To help describe some of these operations, flow diagrams may also be
referenced
to describe combinations of operations that may be performed by a computing
device. In some
cases, a block in a flow diagram may represent a module or portion of program
code that includes
instructions that are executable by a processor to implement specific logical
functions or steps in
a process. The program code may be stored on any type of computer-readable
medium, such as
non-transitory computer readable media (e.g., data storage 204 (FIG. 2)). In
other cases, a block
in a flow diagram may represent circuitry that is wired to perform specific
logical functions or
steps in a process. Moreover, the blocks shown in the flow diagrams may be
rearranged into
different orders, combined into fewer blocks, separated into additional
blocks, and/or removed,
based upon the particular embodiment. Flow diagrams may also be modified to
include additional
blocks that represent other functionality that is described expressly or
implicitly elsewhere herein.
1. Digital Context Creation
[113] To help illustrate how a computing system (such as back-end platform
102) may
establish a digital context, such as digital context 310, reference will now
be made to flow diagram
500 of FIG. 5, which depicts an example process for stabling a digital context
carried out in
accordance with the disclosed digital duplicate technology.
[114] Turning first to block 502, the computing device may first create a
domain for a
particular organization. As indicated a domain may serve to identify that a
particular set of data is
specific for a particular organization. To this end, as part of creating a
domain for a particular
organization, the computing device may also assign a unique identifier (e.g.,
a URI or the like) for
the domain. As also indicated, domains may include zero or more subdomains for
describing
additional aspects of an organization's business, such as individual
operational departments. Thus,
as part of creating the domain, the computing device may create one or more
subdomains, as
desired, and assign unique identifiers to these subdomains (e.g., UltIs or the
like).
[115] The computing device may create a domain by, for instance, receiving
a user input
or a set of user inputs from a client station 112. For instance, the computing
device may cause a
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client station 112 to present a graphical user interface (GUI) of some kind,
through which a user
may provide a user input or a set of user inputs providing information or
instructions to facilitate
the computing device creating a domain for a particular organization. As one
example, a user input
may provide the computing system with various information to create the
domain, such as the
name of the domain, the name of the organization, and the name of any
subdomains, among other
examples. As another example, the user input may provide an indication and/or
instructions for
how the computing device should retrieve from one or more external data
sources various
information to create the domain. For instance, such an indication or
instructions may include an
identification of one or more external data sources at which domain
information can be retrieved.
In some implementations, a combination of user inputs may be provided to the
computing device
to facilitate creating the domain, including one or more user inputs that may
provide the computing
system with various information to create the domain, such as the name of the
domain, the name
of the organization, and the name of any subdomains, and one or more user
inputs that may provide
an indication and/or instructions for how the computing device should retrieve
from one or more
external data sources various information to create the domain. Other examples
of user inputs may
be possible as well.
[116] Turning next to block 504, the computing device may create or enroll
an existing
dictionary for the organization. As indicated, a dictionary may store data
that describes the digital
context for one specific organization. Depending on the implementation, a
dictionary may be
created for an organization from scratch, such as by engaging in one or more
functions described
by the remaining blocks in the flow diagram 500, or all or part of a
dictionary may be enrolled
based on a dictionary for an existing organization.
[117] In the case that all or part of a dictionary will be enrolled based
on a dictionary for
an existing organization, the computing device may do this by selecting the
organization or
organization from which all or part of a respective dictionary will be
enrolled, and then selecting
all or part of the dictionary for the selected organization. To facilitate,
the computing device may
cause a client station 112 to present a GUI of some kind, through which a user
may provide a user
input or a set of user inputs providing information or instructions to
facilitate the computing device
selecting the organization or organization from which all or part of a
respective dictionary will be
enrolled, and then selecting all or part of the dictionary for the selected
organization. As one
example, the client station 112 may present a selection menu presenting
possible organizations
that may be selected, and once an organization is selected, the client device
112 may present a
selection menu to select that presents possible dictionary entries for that
organization that may be
selected. Other ways to enroll all or part of an existing dictionary may be
possible as well.
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[118] In the case that a new dictionary for an organization will be created
from scratch,
the computing device may do this in various ways. As one possibility, the
computing device may
cause a client station 112 to present a GUI of some kind, through which a user
may provide a user
input or a set of user inputs providing information or instructions to
facilitate the computing device
creating a dictionary. Such a user input or user inputs may be provided as
described in the
following blocks.
[119] For instance, at block 506, the computing device may define the
conceptual
components and properties thereof. The computing device may define the
conceptual components
in various ways. As one possibility, the computing device may cause a client
station 112 to present
a GUI of some kind, through which a user may provide a user input or a set of
user inputs providing
information or instructions to facilitate the computing device defining the
conceptual components.
As one example of this, the client station 112 may present a selection menu
presenting possible
conceptual components and possible properties that may be selected. As another
example, the
computing device may cause a client station 112 to present a GUI with one or
more form fillable
fields though which a user may enter information defining the conceptual
components and
properties thereof. Other ways to define conceptual components may be possible
as well.
[120] At block 508, the computing device may define associative components
and
properties thereof. The computing device may define the conceptual components
in various ways,
which may be similar to the ways the computing device defined the conceptual
components. As
one possibility, the computing device may cause a client station 112 to
present a GUI of some
kind, through which a user may provide a user input or a set of user inputs
providing information
or instructions to facilitate the computing device defining the associative
components. As one
example of this, the client station 112 may present a selection menu
presenting possible
associative components and possible properties that may be selected. As
another example, the
computing device may cause a client station 112 to present a GUI with one or
more form fillable
fields though which a user may enter information defining the associative
components and
properties thereof. Other ways to define associative components may be
possible as well.
[121] And at block 510, the computing device may assign to each property of
each
component a semantic data type and a structural data type. The computing
device may define the
conceptual components in various ways, which may be similar to the ways the
computing device
defined the conceptual components. As one possibility, the computing device
may cause a client
station 112 to present a GUI of some kind, through which a user may provide a
user input or a set
of user inputs providing information or instructions to facilitate the
computing device defining the
associative components. As one example of this, the client station 112 may
present a selection
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menu presenting possible associative components and possible properties that
may be selected.
As another example, the computing device may cause a client station 112 to
present a GUI with
one or more form fillable fields though which a user may enter information
defining the
associative components and properties thereof. Other ways to define
associative components may
be possible as well.
2. Digital Content Creation
[122] A computing system (such as back-end platform 102) may facilitate the
creation
of digital content by a process of ingesting organization data and storing it
as an instance of the
digital context. To illustrate how the system may ingest data into the system
and thus store it as
an instance of a digital duplicate in accordance with one example of the above-
described schema,
reference is made to a flow diagram 600 of FIG. 6. At a high level, the
ingestion process may
involve contextualizing and storing in the data store each piece of inbound
data. Generally, this
process may be used to ingest data for (a) an initial population of data to
form an initial instance
of a digital duplicate, and (b) subsequent automated or manual refreshes of
the digital duplicate
instance from all data sources on a regular cadence (e.g. every 10 minutes,
although other
examples may be possible).
[123] Turning first to block 602, a computing device may first identify and
enroll source
data that will be used to populate the digital context to form a digital
duplicate. As a general
matter, the computing device may do this by establishing connections to one or
more data sources
so that the computing device can begin to ingest an organization's data from
these one or more
data sources. For instance, the computing device may cause a client station
112 to present a GUI
through which a user may provide a user input or set of user inputs that
provides to the computing
device data source configuration information, including for instance, data
source location
information (e.g., IP address or other network address), data format
information, data timing
information identifying how often the computing device will ingest new data
from the data source
(e.g., 10 minutes), and any credentials necessary for accessing the
organization's data at the data
sources. Using this information, the computing device may connect to and begin
to ingest the
organization's data. Other ways to identify and enroll data sources may be
possible as well.
[124] After the data is initially ingested, the computing device may
contextualize the
ingested data, which at a high level generally includes assigning or updating
a version number or
timestamp to the data, assigning a semantic data type to the data, assigning a
structural data type
to the data, and/or assigning a URI to the data. Thus, turning next to block
604, the computing
device may assign or update a version number or timestamp to the data. For
instance, when the
computing device initially ingests a piece of data from a given data source,
the computing device
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may assign a version number and/or mark or otherwise associate the data with a
timestamp that
indicates the time of ingestion. When the computing device updates this
ingested data by receiving
a new version of the piece of data from the data source, the computing device
may increment the
version number and/or update the timestamp.
[125] At block 606, the computing device may assign a semantic data type to
the ingested
data. The computing device may do this in various ways. As one possibility, as
part of the
identification and enrollment of the data source (block 602), the computing
device may have
established that a particular semantic data type should be applied to various
data ingested from
the particular data source. For instance, a user may have provided one or more
user inputs at this
step indicating that some or all data ingested from the particular data source
should have a
particular semantic data type assigned thereto. In this case, the computing
device may assign this
semantic data type to the ingested data. As another possibility, the computing
device may cause a
client station 112 to present to a user a GUI through which the user may
select certain ingested
data and then select or otherwise provide a user input that assigns a semantic
data type to that
ingested data. Other ways to assign a semantic data type to the ingested data
may be possible as
well.
[126] At block 608, the computing device may assign a structural data type
to the
ingested data. The computing device may do this in various ways, similar to
the possibilities
described above. As one possibility, as part of the identification and
enrollment of the data source
(block 602), the computing device may have established that a particular
structural data type
should be applied to various data ingested from the particular data source.
For instance, a user
may have provided one or more user inputs at this step indicating that some or
all data ingested
from the particular data source should have a particular structural data type
assigned thereto. In
this case, the computing device may assign this structural data type to the
ingested data. As another
possibility, the computing device may cause a client station 112 to present to
a user a GUI through
which the user may select certain ingested data and then select or otherwise
provide a user input
that assigns a structural data type to that ingested data. Other ways to
assign a structural data type
to the ingested data may be possible as well.
[127] At block 610, the computing device may assign URIs or other
identifying
indicators to the ingested data. Assigning URIs or other identifying
indicators to the ingested data
may serve various purposes and provide various advantages. For instance,
assigning URIs or other
identifying indicators to the ingested data may serve to associate the
ingested data with various
properties for the digital context and thus may associate the data with the
appropriate semantic
data type for each individual property. As another example, assigning URIs or
other identifying
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indicators to the ingested data may, associate the data with appropriate
conceptual components of
the structural context. And as another example, assigning URIs or other
identifying indicators to
the ingested data may and serve to associate the ingested data with
associative components of the
structural context. The computing device may then persistently store the
ingested data in the data
store (e.g., data store 303) along with the assigned UltIs.
B. Example Organizer Operations
[128] As mentioned above, an organizer tool (which may be embodied by a
software tool
running on a computing device) may be used to make a connection between the
semantic network
and an organization's underlying data stores and may be configured to engage
in certain
functionality, including linking multiple data sources to the semantic
network, and/or onboarding
the underlying data from the organization's data stores to an organizer data
store and ultimately
into the semantic network after filtering, cleaning, transforming, and/or
validating the data as
desired.
[129] Example operations that may be carried out by one or more computing
devices
running the disclosed organizer software tool are discussed in detail below.
For purposes of
illustration only, these example operations are described as being carried out
by a computing
device, such as computing device 200 (FIG. 2), which as described above, may
serve as one or
more of client stations 113 (FIG. 1B) and/or back-end platform 112 (FIG. 1B).
In this respect, it
should be understood that, depending on the implementation, the operations
discussed herein
below may be carried out entirely by a single computing device, such as one or
more of client
stations 113 or by back-end platform 112, or may be carried out by a
combination of computing
devices, with some operations being carried out by back-end platform 112 (such
as computational
processes and data-access operations) and other operations being carried out
by one or more of
client stations 113 (such as display operations and operations that receive
user inputs). However,
other arrangements are possible as well.
[130] To help describe some of these operations, a flow diagram may also be
referenced
to describe combinations of operations that may be performed by a computing
device. In some
cases, a block in a flow diagram may represent a module or portion of program
code that includes
instructions that are executable by a processor to implement specific logical
functions or steps in
a process. The program code may be stored on any type of computer-readable
medium, such as
non-transitory computer readable media (e.g., data storage 204 (FIG. 2)). In
other cases, a block
in a flow diagram may represent circuitry that is wired to perform specific
logical functions or
steps in a process. Moreover, the blocks shown in the flow diagrams may be
rearranged into
different orders, combined into fewer blocks, separated into additional
blocks, and/or removed,
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based upon the particular embodiment. Flow diagrams may also be modified to
include additional
blocks that represent other functionality that is described expressly or
implicitly elsewhere herein.
[131] As noted above, in one aspect, the organizer software tool may engage
in a "data
onboarding process" by which it may cause a computing device to, among other
functions, carry
out a process that facilitates connecting to various data sources that house
an organization's data,
establishing pipelines that operate to onboard the organization's data to an
organizer data store
and apply filter, clean, and/or transformation operations that help map the
organization's data to
a semantic network established for the organization This process may take
various forms.
[132] With reference now to flow diagram 700 of FIG. 7, one example of a
data
onboarding process carried out in accordance with the disclosed organizer
software tool is
illustrated and described. This process may generally involve the following
operations: (i) at block
702, the computing device may create a cloud instance for the digital content,
(ii) at block 704,
the computing device may identify and connect to the data sources used for the
digital content,
(iii) at block 706, the computing device may retrieve the data models (which
may generally be
information that identifies the column structure and general format of data
stored at the data
source) from the data sources and import the data models into the software
tool, (iv) at block 708,
the computing device may establish filter and/or clean operations to be
performed on the source
data, (v) at block 710, the computing device may import the source data to an
organizer data store
and apply the established filer and/or clean operations, (vi) at block 712,
the computing device
may define a set of transformations to apply to the source data, (vii) at
block 714, the computing
device may export the data from the organizer data store to the semantic data
store and apply the
defined transformations, and (viii) at block 716, the computing device may
establish an update
schedule by which the system will refresh the semantic data store. Each of
these operations will
now be discussed in further detail with reference, in some cases, to example
snapshots of GUIs
that may facilitate some or all of the disclosed functionality.
[133] Turning first to block 702, the computing device may begin the data
onboarding
process by creating a cloud instance for the digital content. In one
implementation, this may take
the form of establishing a cloud network storage location wherein a particular
organization's data
may be stored. This may include, for instance, establishing or reserving a
portion of a cloud
network storage location or other data store to serve as either or both of the
organizer data store
114B and/or the semantic network data store 114C (FIG. 1B). This cloud network
data store may
be provided either by the owner or operator of the back-end platform 112 or by
a third-party
provider of "on demand" cloud storage, such as Amazon Web Services (AWS), or
the like. To
facilitate this process, the organizer software tool may present one or more
GUI screens through
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which a user may enter various identifying information for the organization
and/or business unit
or division of the organization and then use this information to establish an
appropriate cloud
instance for the digital content. A computing device may create a cloud
instance for digital content
in other ways as well.
[134] Next at block 704, the computing device may identify the
organization's various
disparate data sources (represented in one example in FIG. 1B by business data
sources 114A or
in another example in FIG. 1A by data sources 106), which are desired to be
used as sources for
the digital content. The computing device may do this in various ways. As one
possibility, the
computing device may present a GUI through which a user may provide various
user inputs that
indicate how the computing device can connect to the one or more
organization's various disparate
data sources. For instance, for each data source desired to be connected to
and used as a source
for the digital content, a user may provide a user input that indicates a name
for the data source
(e.g., "customer database," employee list," etc.), the type of data source
(e.g., Microsoft Excel
workbook, SAP HANA, Microsoft Access database, Oracle database, SQLServer
database, a web
service, TADA database, etc.), the IP address or other location-identifying
information of the data
source, credentials or other security information for accessing the data
contained within the data
source, and/or information identifying the type or format of the data stored
within the data source.
A user may provide other information that facilitates the computing device
connecting to an
organization's disparate data sources as well.
[135] As another possibility, the computing device may receive a file
upload that
comprises a data source. As an example, the computing device may receive a
spreadsheet provided
via computer readable media, such as a flash drive or disc. To facilitate
receiving a file upload
that comprises a data source, the computing device may present a GUI through
which a user may
provide various inputs that instruct the computing device where to retrieve
the file from. The
computing device may identify and connect to an organization's various
disparate data sources in
other ways as well. Once the computing device identifies and/or connects to a
given data source,
that data source may be said to be "enrolled." It should be appreciated that,
in practice, multiple
data sources may be "enrolled" by the disclosed software tool, each of which
may ultimately
provide underlying data to the platform.
[136] Next at block 706, the computing device may then connect to the
identified data
sources, identify the data models used at the data sources, and import the
data models into the
platform. . For example, for data stored in a typical relational database, the
data model for that
database may include information that identifies how many tables are included
within the
database, how many columns there are for each table, the name of each column,
the data type of
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the records that are or will be stored within the data tables (e.g.,
"integer," "string," "decimal,"
"datetime," etc.), and/or other information that further defines the data
model, such as allowable
length of a data entry stored at each column, or other rules for storing data
at each column such as
whether leading or trailing spaces are allowed.
[137] In some implementations, once the computing device has retrieved the
data models
from the one or more data sources, the computing device may present a GUI that
displays a listing
of the one or more data sources, the various tables available at each data
source, and a navigable
interface that allows a user to explore the data model of each data source. To
illustrate one example
of this, FIG. 5 depicts an example snapshot 500 of a GUI that displays a
listing of the data sources,
the various tables available at each data source, and a navigable interface
that allows a user to
explore the data model of each data source. As depicted, the GUI may include a
"source table"
graphical location 506 that may display indications of the various data
sources that have been
enrolled and are thus available to the computing device, including what tables
are included in each
data source and which columns are included within each table. As depicted in
the example GUI
of FIG. 5, the source table location 506 shows a "VPData" data source that
includes several tables
including a "VPData AssetOper" table and a "VPData AssetOperDaily" table,
among other
displayed tables. The example GUI may use a "nested tree structure" layout to
arrange the source
table graphical location 506 such that the tables of a given data source are
nested underneath the
name of the data source, and columns of each table are nested underneath each
table name. Other
ways to display the columns, tables, and data sources available to the
computing device may be
possible as well.
[138] Returning to FIG.7 at block 708, the computing device may next
establish filter
and/or clean operations to be performed on the source data prior to or in
conjunction with
onboarding the data. Many types of filter/clean operations are possible. For
example, a filter/clean
operation may specify which portions of the source data to import from the
various data sources
and which portions not to import. A filter/clean operation may also specify,
for instance, whether
to change the name or data type of any aspect of the data model during the
onboard process. For
instance, one example filter/clean operation may be an operation that changes
the name of a
column from something like "ShNm" to "Shift Name" or from "AID" to "Asset ID."
Another
example filter/clean operation may be an operation that changes a data type
for a column from
"float" to "integer" or from "string" to "nvarchar." Yet another example
filter/clean operation
may be an operation that changes the length of the data input from, say, "500"
to, say, "20." And
yet another example filter clean operation may be an operation that specifies
to import only those
data records that match a particular query filter, such as records that have
an "InstID" value of
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"28." These were merely examples, however, and other types and examples of
filter/clean
operations may be possible in practice. One advantage of applying a
filter/clean operation at this
point in the process is that the data can be preconditioned to a known data
type for further
manipulation, independent of its original data type. Additionally, any errors
in the data source can
be identified during the onboarding process (e.g., if not all items in a
column share the same data
type, an error will be raised at this point).
[139] To facilitate establishing filter and/or clean operations, such as
the forgoing
examples as well as other possible examples, the computing device may present
a GUI through
which a user may provide various user inputs in order to specify one or more
filter/clean operations
to employ. To illustrate with an example, returning to snapshot 800 of FIG. 8,
the GUI depicted
includes an example "filter/clean" graphical location 804 and a "table
interface" graphical location
802.
[140] Referring first to filter/clean graphical location 804, this location
shows various
information about the data model and any established filter/clean operations
for a selected table
from the data source. As depicted in FIG. 8, for instance, the example
filter/clean graphical
location 804 depicts various information related to the "VPData AssetOper"
source data table.
As further depicted in filter/clean graphical location 804, for instance, the
GUI may include a
"Name (Source Table)" column 810 that shows the name of the various columns as
they exist in
the source data table, a "Name (Column Interface)" column 808 that shows the
name of the
corresponding column as it will be identified in the (new) table and column
interface (i.e., when
imported into the organizer data store), a "Source Data Type" column 812 that
shows the data
type for the column as it exists in the source data table, an "Interface Data
Type" column 814 that
shows the (new) data type for the corresponding column as it will exist in the
table and column
interface (i.e., when imported into the organizer data store), and a "Length
or Scale" column 816
that shows the (new) length or scale of the data for the corresponding column
as it will exist in the
table and column interface (i.e., when imported into the organizer data
store).
[141] Portions of the filter/clean graphical location 804 may be configured
to receive
user inputs in order to establish certain filter/clean operations. For
instance, in this example
columns 808, 814, and 816 may be editable so as to provide the ability to
change the name of
columns, change the data type, and/or change the length of data, respectively,
of the data tables of
the data source prior to importing the data from the data source into the
organizer data store. As
an example, a user may enter text into the various rows of columns 808 or 816
in order to change
the name of a given column of a source data table or change the length or
scale of the data stored
for that column. The various rows of column 814 may include drop down boxes
that contain
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various example data types that may be selected by a user to change the data
type for a given
column of a source data table. The filter/clean graphical location 804 may
include other columns
as well so as to provide the ability to view and/or edit one or more other
properties of the data
model of a data source prior to importing the data from the data source into
the organizer data
store. In this way, a user may navigate through the various data tables of the
data source via the
filter/clean graphical location 804 and provide various user inputs (e.g., via
columns 808, 814,
and/or 816, among, perhaps, other possible columns that are not shown) to
establish a set of
filter/clean operations to apply to the source data as it is imported into the
organizer data store.
[142] Once a set of filter/clean operations are established for one or more
data tables of
a particular data source, the computing device may engage in some additional
functionality. For
instance, in some implementations, the computing device may determine a set of
similar
filter/clean operations to apply to other data tables in the same or different
data source based on
the set of filter/clean operations that were established above by the
computing device, for instance,
receiving via a GUI one or more user inputs that define a set of filter/clean
operations. The
computing device may do this in any of a number of different ways. As one
possibility, for
instance, for a set of filter/clean operations that change the name of a
column from "AID" to
"Asset ID," and that change a data type for this column from "decimal" to
"integer," and that
changes the length of the data input from "500" to "20," the computing device
may first search
other source data tables that have columns that are similarly named (e.g., for
columns that are
named "AID," "AssetID," "Asset ID" or the like) and have similar source data
formats (e.g.,
columns that may have data formats of "decimal" and/or have length at or near
"500"). Once any
such columns are found in other source data tables, the computing device may
apply the same
filter/clean operations to these data columns, or the computing device may
present to a user though
a GUI a dialog box or the like recommending these filter/clean operations and
prompting the user
to approve the set of recommended filter/clean operations. As another
possibility, the computing
device may recognize an existing filter/clean operation to apply as a result
of a user entering in a
few characters of a particular column. For instance, for a new filter/clean
operation where a user
enters "Asse" into the "Name (Column Interfaces)" column 808, the computing
device may
suggest an existing set of filter/clean operations where at least the phrase
"Asse" appears, such as
a set of filter/clean operations that change the name of a column from "AID"
to "Asset ID," and
that change a data type for this column from "decimal" to "integer," and that
changes the length
of the data input from "500" to "20."
[143] In some implementations, the computing device may generate what is
referred to
as a "table interface" having one or more "column interfaces." The computing
device may
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generate one table interface for every source data table that will be
onboarded into the system and
stored in the organizer data store. Likewise, for every column in a particular
source data table that
will be onboarded into the system and stored in the organizer data store, the
computing device
may generate a corresponding column interface. These column interfaces and
table interfaces are
generally data structures that define or contain, among other things, (i)
information regarding
where in the source data stores to get the data that forms the particular
column or table interface,
and (ii) the set of zero or more filter/clean operations that may be applied
to the source data as it
is imported into the organizer data store. In this way, a set of all table
interfaces for a particular
organization can be thought of together as a "data interface," which serves as
the input for the rest
of the data operations carried out (or facilitated) by the disclosed organizer
software tool.
[144] In some embodiments, the computing device may present a GUI or
portion of a
GUI that displays the list of table and column interfaces for a given project.
Returning to the
example GUI of FIG. 8, as mentioned this GUI may include a table interface
graphical location
802, which displays the list of table interfaces and column interfaces in a
nested format. Each table
interface may be selectable such that when a given table interface is
selected, the computing device
may display in filter/clean graphical location 804 a corresponding set of
information regarding the
data model and any selected filter/clean operations for the corresponding
table from the source
database.
[145] Generating a data interface, which comprises table interfaces and
column
interfaces as referred to above, may provide several advantages. For instance,
as mentioned, the
data interface can be thought of as serving as the input for the remainder of
the data operations
carried out (or facilitated) by the organizer software tool. In this way, the
remainder of the data
operations will not depend on the underlying data source directly but rather
will be "shielded" by
the data interface. In this way, if the organization makes a change to some
part of the underlying
source data (such as changing a column name or a column's data type), this
change may not
corrupt the rest of the downstream operations of carried out (or facilitated
by) the organizer
software tool, as those operations refer to and thus utilize the table and
column interfaces rather
than the source data directly.
[146] Returning to FIG. 7 at block 710, the computing device may next
import the
underlying source data records to an organizer data store (e.g., organizer
data store 114B) and
apply the filter/clean operations that were established in accordance with the
previous block 708.
For instance, in one embodiment, as the data is being imported into the
organizer data store, the
computing device may apply the filter/clean operations and store the data in
the organizer data
store after applying the set of established filter/clean operations.
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[147] In an alternative embodiment, the computing device may not import the
underlying
source data records to an organizer data store before applying the
filter/clean operations. Rather,
the computing device may apply the filter/clean operations to the data as it
is stored in the
underlying data source. As a result, the computing device may determine if
there are any
exceptions or errors that result from these operations and, if there are,
present these exceptions or
errors to a user for resolution. The set of filter/clean operations can be
stored in a data store, such
as organizer data store 114B.
[148] Moving on to block 712, the computing device may next define a set of
transformations to apply to the source data stored in the organizer data
store. As a general matter,
a transformation may be any change, manipulation, or combination of data,
and/or assignment of
the data to an aspect of the semantic data network, such as a node or a link
in a digital context.
Transformations can be thought of as operations that re-shape the input data
and form the basis of
pipelines that in turn can be thought of as moving data into the semantic
network. More specific
examples of transformations may include, for instance, removing duplicates,
converting data
types, removing certain characters from data, concatenating columns,
extracting dates, filtering
rows, aggregating rows, merging tables, creating a new virtual table or data
source, and/or
assigning data to an aspect of the semantic network, such as a node or link in
the semantic network,
among other possibilities. Transformations may be applied to the data stored
in the organizer data
store in various ways. As one possibility, transformations may be applied to
data from a single
data table from a single data source. As another possibility, transformations
may be applied to
data from multiple data tables from multiple data sources. A set of
transformations established for
a particular data table may be referred to as a "sequence" and a set of
sequences for all the tables
in a particular data source may be referred to as a "data contract."
[149] To facilitate establishing transformations to apply to the source
data stored in the
organizer data store, the computing device may present a GUI through which a
user may provide
various user inputs in order to select or otherwise define the set of
transformations to apply for a
particular data table or data source. To illustrate with an example, FIG. 9 is
an example snapshot
900 of a GUI that displays a "data source" graphical location 902 that is
configured to display data
tables and data sources that have been imported into the organizer data store
(or created via a
transformation, as will be described herein), a "data contract" graphical
location 904 that is
configured to display the set of transformations for each data table of the
data sources that have
been imported into the organizer data store, and a "node/link" graphical
location 906 that is
configured to display the nodes, links, and/or properties of the semantic
network.
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[150] Within the data contract graphical location 904, for instance, the
computing device
may display the set of transformations that have been established for a
particular data table. As
depicted in FIG. 9, for instance, the data contract graphical location 904
depicts indications that
transformations that have been established for the "VPData Assets," VPSYS
Instances," and
"VPData LoadCount" data tables. In some embodiments, the computing device may
provide the
ability to select indications of the transformations (e.g., "VPData Assets
Node Model") and
responsively display further details on the transformation as well as provide
an area within which
a user can modify the transformations or add new transformation. For instance,
upon the selection
of an "Add Transform" button or the like, the computing device may provide a
listing of possible
transformations to select from to apply to some or all the data in a
particular data table. As depicted
in FIG 9, for instance, an "Assign Node" transform has been established for
the "Model" column
in data table "VPData Assets" from data source "VPData." The data from the
"Model" column
has been assigned to the "Model" node in the semantic network.
[151] As mentioned, one type of transformation for a data table may result
in some or all
of the data table being resolved into a new, "virtual" data source referred to
in some examples as
a "from contract" data source. A virtual data source may then be treated as if
it were a traditional
data source connected to by the computing device, in that additional
transformations may be
applied to data tables and columns of the virtual data source, such as
assigning data columns to
nodes, links, and properties of the semantic network. As depicted in FIG. 9,
for instance, the data
source graphical location 902 depicts a "From Contract" virtual data source
having several virtual
data tables, including "AO Short Term," "AssetDaily," "LC Short Term," and
"Short TermDate,"
although in practice others are possible.
[152] It may be advantageous to apply a transformation to one or more data
tables of a
data source to form a virtual data source in instances where it has been
identified that a particular
kind of data would be desirable, but the organization does not store that type
of data. For instance,
an organization may store data in a first data table related to customer
information, such as the
customer name and customer address, and may store data in a second data table
related to
purchases made by customers, such as date of purchase, products purchased,
quantity purchased,
etc. The organization may not necessarily, however, store roll-up data that
aggregates purchase
information and customer information. It may be advantageous to establish such
roll-up data in a
virtual data source, in order to help prevent the number of transformations
applied to a given data
table from becoming unwieldy, help an operator or other subject matter expert
visualize the data,
help assist an operator in applying additional transformations, such as
assigning the roll-up data
to a node, link, or property in a semantic network, among other possible
advantages. As an
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example, by establishing an appropriate set of transformations, a user may
enroll a virtual data
source that includes, say, a first table that aggregates by month all the
purchases made from
customers who are located in midwestern states, east-coast states, southern
states, etc., and, say, a
second table that aggregates by product all the purchases made by customers on
a more granular
geographic level, such as by state or even city. Of course, this is merely an
example; in practice
there may be many other types of virtual data sources that can be established
as well as many other
reasons for establishing a virtual data source.
[153] As transformations are established for the various data tables of the
enrolled data
sources, the computing device may engage in some additional functionality. For
instance, based
on a first set of transformations applied to a first set of data, the
computing device may identify a
second set of data with names or other properties similar to names or other
properties of the first
set of data and the computing device may establish a second set of transforms
for the second set
of data that are similar to the first set of transforms. To illustrate with an
example, one table of
one data source may contain data related to an organization's customers. This
data table may
include columns such as "CustomerID," "CustomerName," "CustomerAddress,"
"CustomerCity," "CustomerState," "CustomerCountry," etc. A second table of a
second data
source may also contain data related to the organization's customers and
include such columns as
"CustID," "CustName," "CustPaymentTerms," etc. In this example, if the
computing device
establishes a transformation (e.g., through a user providing a suitable user
input via a GUI)
whereby the column "CustomerID" from the first table of the first data source
is assigned to the
"customer" property of the "customer" node in the semantic database, then the
computing device
may use that transformation as a basis to establish similar transformations.
For instance, the
computing device may recognize that the other columns in the same data table
also contain the
same word "Customer" as the first column and may thus use this as a basis to
establish
transformations for these columns that assign these columns to various other
properties in the
"customer" node.
[154] Similarly, the computing device may recognize that the second table
of the second
data source also contains columns each with the prefix "Cust" in them (which
the computing
device may determine is similar to "Customer") and each column contains
similar suffixes to the
columns of the first data table in the first data source (e.g., "ID" and
"Name"). The computing
device may thus use this as a basis to also establish transformations for
these columns that are
similar to the transformations established for the first data table in the
first data source (i.e., assign
these columns to various other properties in the "customer" node).
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[155] The computing device may also take into account other factors in
determining
whether to establish certain transformations for a particular set of data. For
instance, in one
embodiment, when determining whether to establish or suggest transformations
for a first set of
data columns, the computing device may compare the underlying data records for
the first set of
data columns to underlying data records for another set of data columns (e.g.,
from the same or
different data source) to determine whether the records contain a threshold
number of similar
entries. If the computing device determines that the underlying data records
contain such a
threshold number of similar entries, then the computing device may use this as
a basis for applying
the same transformations to the first set of data columns that have been
established for the other
set of data columns. In some embodiments, the computing device may not
automatically establish
transformations but rather present suggested transformation to apply and,
through a GUI, present
a user with the option to adopt some or all of the suggested transformations.
The computing device
may establish or suggest transformations in other ways and may also engage in
other functionality
as well.
[156] In addition to establishing transformations, the computing device may
also
determine whether discrepancies exist in an organization's data and may
responsively highlight
these discrepancies and present an option or a suggestion to address the
determined discrepancy.
For instance, if one data table contains a first column name "CustomerName"
with a data record
of "AjaxPlumbing" and a second column name "Customer ID" with a corresponding
data record
of "255", and another data table contains a first column name "CustName" with
a data record of
"AjaxP" and a second column name "CustID" with a corresponding data record of
"255," the
computing device may determine that the columns "Customer ID" and "CustID" are
sufficiently
similar in name such that they refer to the same semantic concept. Thus, the
computing device
may recognize that data records for each column contain a similar entry
(namely, "255") and may
thus determine that the data corresponding to the other columns, namely
"CustName" and
"CustomerName" likely refer to the same entity. However, the computing device
may also
determine that underlying data records corresponding to the Customer ID "255"
for each data
table (namely, "AjaxP" and "AjaxPlumbing") are not sufficiently similar.
Responsively, the
computing device may indicate this discrepancy to a user, such as by
presenting the discrepancy
via a GUI. The computing device may also present the user with the option to
address this
discrepancy by, for instance, adopting one version of the customer name over
the other. For
instance, the computing device may provide the user with the option to select
one of the versions
of the customer name, and upon receiving a user input selecting one of the
customer names, the
computing device may establish a transformation that operates to apply the
selected customer
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name in place of the non-selected customer name. The computing device may
address data
discrepancies in other ways as well.
[157] As mentioned, a set of transformations established for a particular
data table may
be referred to as a "sequence" and a set of sequences for all the tables in a
particular data source
may be referred to as a "data contract." In some embodiments, the computing
device may in one
or more ways present visualizations of the various data contacts established
for a given
organization. One example visualization may take the form of a data contract
tree view, which is
depicted by the example snapshot 1000 in FIG. 10. As depicted in location
1002, for instance, the
computing device may present the various transformations that have been
established for each
source data table in a nested tree format. This nested tree format may, among
other things, indicate
for each data table, the name and type of transformation established and may
further indicate any
additional details about each established transformation, such as the node,
link, or property of the
semantic network the data is assigned to. Other ways to present visualizations
of the various data
contacts established for a given organization may be possible as well.
[158] Returning to FIG. 7 at block 714, the computing device may next apply
the
established transformations to the source data stored in the organizer data
store and then export
the transformed data and store it in a semantic data store (e.g., sematic data
store 114C). This
action can be thought of as pairing the organization's data with the digital
context. As such, once
the data has been transformed and stored in the semantic data store, it
combines with the digital
context to form an instance of a digital duplicate. In one embodiment, as the
data is stored in the
semantic data store, the computing device may assign to each instance of the
data a unique
identifier that identifies the location within the semantic data store at
which each data instance is
located. For instance, this unique identifier may take the form of a unique
URL or an identifier of
a location within a SQL or NoSQL table, among other possibilities. In some
embodiments, the
transformations are not yet applied to the source data but, rather, data
describing the
transformations are stored in a data store, such as organizer data store 114B,
and later applied to
the source data as it is onboarded into the platform from the underlying data
sources 114A during
a batch update, or the like.
[159] Next at block 716, the computing device may establish an update
schedule by
which the computing device system will refresh the data stores. An
organization's data may
change over time. For instance, an organization may delete data records,
reformat data models,
and/or add new data sources altogether. To account for this, the disclosed
organizer software tool
may provide the ability to schedule updates to the various data stores,
whereby the computing
device periodically engages in some or all of the forgoing functionality
according to an established
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schedule. For instance, the computing device may periodically engage in
applying filter/clean
operations and importing source data into the organizer data store as well as
engage in applying
transformations to data stored in the organizer data store and exporting the
transformed data from
the organizer data store to the semantic data store. One advantage to
establishing an update
schedule is that even when new underlying data sources are enrolled or have
modified filter/clean
operations or transformations established, the update can happen seamlessly
without a user having
to rebuild any filter/clean operations or transformations.
[160] In some embodiments for instance, the computing device may apply a
periodic
update schedule on a per-data-source basis. For instance, if an organization
updates data (e.g.,
purchasing data) in a first data source on an hourly basis, but updates data
(e.g., customer data) in
a second data source on a daily (or an even less-frequent basis) basis, the
computing device may
take advantage of this knowledge to engage in update actions that involve the
first data source
more frequently (e.g., hourly) than it may engage in update actions that
involve the second data
source (e.g., which may occur daily or weekly, as examples). To facilitate
this, the computing
device may present a GUI or a set of GUIs through which a user may provide
various user inputs
that the computing device may use to establish one or more update schedules
for an organization's
data. Other ways of establishing an update schedule by which the computing
device system will
refresh the data stores are possible as well.
[161] In some embodiments, the computing device may establish a trigger
event, upon
the occurrence of which the computing device will refresh the data stores.
Many different types
of trigger events are possible. For instance, one type of trigger event may
take the form of an
underlying data source being updated. When an underlying data source is
updated with new data
or data in the underlying data source has changed in some way, the computing
device may receive
an indication of such and may responsively engage in refresh actions, such as
applying filter/clean
operations and importing source data into the organizer data store as well as
applying
transformations to data stored in the organizer data store and exporting the
transformed data from
the organizer data store to the semantic data store.as one possibility. Other
types of trigger events
may be possible as well.
[162] In some embodiments, the computing device may cause the system to
store
multiple instances of an organization's data in the various data stores. In
this way, should some
change to the underlying data sources corrupt the organization's data as
stored in either or both of
the organizer data store or the semantic network data store, the computing
device may revert back
to using a previous (uncorrupted) version of the organization's data.
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[163] One way that the computing device may engage in updates to the
various data
stores is to selectively update just the data that has changed in the
underlying data source. To
facilitate this, the computing device may retain a timestamp or other
mechanism that indicates the
most recent time the computing device imported data into the organizer data
store and compare
that against a timestamp or other mechanism retrieved from the underlying data
store that indicates
when a data column or data table in the underlying data store was last
updated. When the
timestamp or other mechanism from the underlying data store indicates that a
set of data was
updated more recently than the computing device imported such data into the
organizer data store,
then the computing device may carry out an update process for just that set of
data that was updated
in the underlying data store more recently. Mechanisms other than a timestamp
may be used to
facilitate the computing device determining whether some set of data from the
underlying data
source has been updated more recently than was imported into the organizer
data store, such as
editable revision fields in the data source, update counters, or even data
fields that contain a flag
or other indication such as a text entry "finished," or the like, that
indicate when an update has
been completed.
[164] In some embodiments, the computing device may engage in data
validation
functions. For instance, as mentioned, an architect tool may be used to
establish the framework
by which an organization's data is set out in the semantic network (i.e., the
digital context). In
some cases, an operator may use the architect tool to define some portion of
the digital context,
switch to use the organizer tool to onboard some portion of the organization's
underlying data,
including establishing filter/clean operations and/or transformations, and
then switch back to the
architect tool to continue modifying or adding new aspects to the digital
context. To address
situations like this, the organizer tool may engage in a validation procedure
by which it identifies
any changes to the structure of the semantic network (such as the presence or
absence of nodes,
links, or properties) that affect or are related to established
transformations. For instance, if a
transformation were established that operated to assign a particular data
column to a particular
property of a particular node in the semantic network, and thereafter a change
occurred to the
semantic network such that the particular property and/or the particular node
were removed or
otherwise changed in the semantic network, then the computing device may
provide to a user an
indication of this change. In some embodiments, the computing device may
present though a GUI
an indication of the specifically affected transformation and may provide one
or more
recommendations on how to address the change (such as by revising the
transformation to assign
the data column to some other property of some other node, among other
possibilities). Other
ways to engage in data validation functions may be possible as well.
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[165] In some embodiments, the computing device may engage in a lineage
feature, by
which the computing device may use the list of transformations in a data
contact to determine the
data table and column(s) that feed each aspect of the semantic network, such
as the nodes,
properties, and links. The computing device may provide one or more
visualization tools that show
the "data lineage" between an aspect of the semantic network and one or more
underlying data
sources. In this way, a user can trace the lineage of an aspect of the
semantic network back to an
underlying data source and identifying transformations to that data along the
way so as to get a
better understanding of how the underlying data is sued to populate the
semantic network. This
lineage feature may be especially valuable in situations where many different
transformations are
applied to the underlying source data before it is mapped to aspects of the
semantic network.
C. Example Security Operations
[166] It may be advantageous to establish restrictions on accessing the
data as it exists in
the semantic network at the semantic network level. For instance, although as
set forth above, a
computing device may be configured to present one or more visualizations of an
entire semantic
network (i.e., presenting a visualization that includes each and every node
and link of the semantic
network), including displaying data related to the underlying data records
that have been assigned
to the nodes or links of the semantic network, it may be advantageous to, at
times, present a
visualization of just a portion of a semantic network (e.g., presenting a
visualization that includes
just a subset of the nodes and links of the entire semantic network),
including, for instance,
displaying data related to the underlying data records that have been assigned
to just a subset of
the nodes or links of the semantic network. This may be advantageous, for
instance, in situations
where an organization desires to give one type of user (e.g., employees)
access to one type of the
organization's data (e.g., employee data) but desires to restrict access of
that type of user to other
types of the organization's data (e.g., company-financial data).
[167] In some systems, data security operations may be applied at the data
source level.
For instance, access to entire data sources may be restricted on a user-by-
user basis with some
users having access to all data stored in a given data source and other users
not having access to
any of the data stored in the given data source. However, this may become
problematic when data
sources are used to store large amounts of an organization's data. In such
cases, a single data
source may, for instance, store multiple types of data (e.g., employee-related
data and company-
financial data), and thus it may be desirable to allow certain users to access
some but not all of the
data stored in a particular data source. Depending on the data source, this
may or may not be
possible. In situations where it is possible, the data security profiles may
be applied on a table-by-
table or column-by-column basis, which is a cumbersome and time-consuming
process to manage.
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In situations where allowing certain users to access some but not all of the
data stored in a
particular data source is not possible, then different types of data may have
to be split out and
stored in separate data sources (e.g., with one data source storing employee-
related data and
another separate data source storing company-financial data, among other
possibilities). This too
may lead to added cost associated with implementing multiple data sources. And
in either case,
managing the security profiles when organizations implement changes in the
underlying data
sources may further increase the time and expense. For instance, when an
organization adds a
column or table in a data source or moves data from one column or table to
another column or
table in the same data source, all security profiles may have to be modified
to account for this
change.
[168] To address these situations, and perhaps others, disclosed herein is
an example
software tool that may facilitate engaging in data security operations for
semantic networks and
applying these data security operations at the semantic network level as
opposed to applying the
data security operations at the data source level. Applying the data security
operations at the
semantic network level rather than at the data source level may result in more
efficient application
of data security operations. Indeed, if data security operations are applied
at the semantic network
level, an organization may make a changes to the underlying data sources (such
as adding a data
source, moving data from one data source to another data source, or even
combining data from
multiple data sources into a single data source) without affecting the
existing application of the
data security operations.
[169] For instance, as will be appreciated with the benefit of the present
disclosure, if an
organization moves, say, employee data from one data source to another data
source that stores,
say, company-financial data, so long as the relationship between the
underlying data and the
semantic network remains unaffected (e.g., the employee-related data remains
assigned to the
various employee-related nodes and links of the semantic network), the
existing data security
profiles will remain unaffected too. In this way, for instance, users
authorized to access only the
employee-related data will continue to be able to access the employee-related
data (even though
it is now stored in another data source) and will not be granted access to the
company-financial
data as a result of the change to the underlying data source. Thus, an
organization may make
changes to the underlying data sources without having to reestablish or
otherwise modify the data
security profiles. These, and other advantages, will become apparent in view
of the entire
disclosure.
[170] As disclosed herein, data security operations applied at the semantic
network level
may include operations for establishing user permissions for accessing various
nodes and links of
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a semantic network, which provides access to the underlying data assigned to
these nodes and
links (such as data related to a digital duplicate, including data related to
the digital context and
the digital content of a digital duplicate), and operations for generating
visualizations related to a
semantic network in accordance with these user permissions. In this respect,
and using the
software tool disclosed herein, an administrator or the like may authorize
particular authorized
users to access the underlying data assigned to certain selected portions of
the semantic network.
These authorized users may then be able to access the data assigned to the
selected portions of the
semantic network, access visualizations related to the underlying data
assigned to the selected
portions of the semantic network, determine insights related to the underlying
data assigned to the
selected portions of the semantic network, and manipulate data related to the
underlying data
assigned to the selected portions of the semantic network. However, the
authorized users may not
be able to access data assigned to other portions of the semantic network
(i.e., portions of the
semantic network that were not selected), meaning that the authorized users
may not be able to
access visualizations related to the underlying data assigned to the other
portions of the semantic
network, determine insights related to the underlying data assigned to the
other portions of the
semantic network, or manipulate data related to the underlying data assigned
to the other portions
of the semantic network.
11711 For purposes of illustration only, example operations are
described herein as being
carried out by a computing device, such as computing device 200 (FIG. 2),
which as described
above, may serve as one or more of client stations 113 (FIG. 1B) and/or back-
end platform 112
(FIG. 1B). In this respect, it should be understood that, depending on the
implementation, the
operations discussed herein below may be carried out entirely by a single
computing device, such
as one or more of client stations 113 or by back-end platform 112, or may be
carried out by a
combination of computing devices, with some operations being carried out by
back-end platform
112 (such as computational processes and data-access operations) and other
operations being
carried out by one or more of client stations 113 (such as display operations
and operations that
receive user inputs). However, other arrangements are possible as well.
[172] To help describe some of these operations, flow diagrams may also
be referenced
to describe combinations of operations that may be performed by a computing
device. In some
cases, a block in a flow diagram may represent a module or portion of program
code that includes
instructions that are executable by a processor to implement specific logical
functions or steps in
a process. The program code may be stored on any type of computer-readable
medium, such as
non-transitory computer readable media (e.g., data storage 204 (FIG. 2)). In
other cases, a block
in a flow diagram may represent circuitry that is wired to perform specific
logical functions or
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steps in a process. Moreover, the blocks shown in the flow diagrams may be
rearranged into
different orders, combined into fewer blocks, separated into additional
blocks, and/or removed,
based upon the particular embodiment. Flow diagrams may also be modified to
include additional
blocks that represent other functionality that is described expressly or
implicitly elsewhere herein.
[173] Turning first to FIG. 11A, this figure presents a flow diagram 1100
depicting one
example of a process for engaging in data security operations for semantic
networks, and in
particular, for establishing, and presenting visualizations related to, a
"realm" of a semantic
network. As depicted, this process may generally involve the following
operations: (i) at block
1102, a computing device may receive an input indicating a desire to create a
realm of a semantic
network, (ii) at block 1104, the computing device may define a primary node
for the realm, and
(iii) at block 1106, the computing device may receive a selection of one or
mode nodes or links of
the semantic network to include in the realm. Each of these operations will
now be discussed in
further detail with reference, in some cases, to example snapshots of GUIs
that may facilitate some
or all of the disclosed functionality.
[174] Turning first to block 1102, the computing device may receive an
input indicating
a desire to create a "realm" of a semantic network. A realm of an
organization's semantic network
is generally a subset of nodes and links of the semantic network, where, in
some embodiments,
the subset of nodes and links relate to some aspect of the organization. For
instance, one type of
realm may be an "employee realm," which may include various nodes and links
that describe data
that ought to be accessible to various employees of the organization (such as
employee data) and
may not include nodes or links that describe data that ought not to be
accessible to various
employees (such as company financial data). As another example, another type
of realm may be
a "customer realm," which may include various nodes and links that describe
data that ought to
be accessible to various customers of the organization (such as customer data
and/or order data)
and may not include nodes or links that describe data that ought not to be
accessible to various
customers (such as employee data and/or company financial data). Other types
of realms are
possible as well.
[175] A computing device may receive an input indicating a desire to create
a realm in
various ways. As one example, a computing device may present via a GUI one or
more screens
through which a user may provide to the computing device various user inputs,
including an
instruction to create a new realm and other various identifying information
for the new realm. To
illustrate one example of this, FIG. 12A depicts an example snapshot 1200 of a
GUI through which
a user may provide a user input indicating a desire to create a new realm. For
instance, as depicted,
the GUI may include a realm navigation panel 1202 that includes an "Add Realm"
button 1204.
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In operation, the computing device may receive a user selection of the "Add
Realm" button (e.g.,
by way of a mouse click or touchscreen tap, among other possibilities) and may
responsively
display an information window 1206 through which the user may provide
additional user inputs
to, for instance, to name the new realm and provide a text description of the
new realm. Other
ways for a computing device to receive an input indicating a desire to create
a new realm may be
possible as well.
[176] Returning to FIG. 11A at block 1104, the computing device may next
define what
is referred to as a "primary node" for the realm. A primary node for a realm
may generally be a
node of a semantic network that contains underlying data records desired to be
used as the basis
to limit access to the underlying data of the realm. For instance, for an
"employee realm," which
may be designed to include nodes and links of a semantic network that ought to
be accessible to
employees of an organization and not include nodes or links that ought not to
be accessible to
employees of the organization, a primary node for this realm may an "employee"
node. In this
way, and as will be described further herein, the underlying data records of
the employee node
may be used as a basis to limit access to the underlying data of the rest of
the realm. For instance,
in one example, a particular data record of the primary node may be selected
(e.g., a particular
employee) and, for a given user or set of users, access to the underlying data
of a realm may be
limited to just those data records that relate to the selected data record of
the primary node. Other
examples of primary nodes are possible as well.
[177] A computing device may define a primary node in various ways. As one
example,
a computing device may receive a user input indicating a particular node in
the semantic network
to use as the primary node for the realm being created. To facilitate this,
the computing device
may, as noted above, present via a GUI one or more screens through which a
user may provide to
the computing device various user inputs, including an indication or a
selection of a node to use
as the primary node for a realm being created. As one example of this, FIG.
12B depicts an
example snapshot 1210 of a GUI that is displaying an information window 1208
listing the various
nodes of the semantic network. Upon selection of one of the listed nodes
(e.g., the "employee"
node as depicted in FIG. 12B), the computing device may then define the
primary node for the
realm being created as this selected node as the primary node for the realm.
Other ways of defining
a primary node for a realm may be possible as well.
[178] Returning to FIG. 11A at block 1106, the computing device may next
receive a
selection of one or more nodes or links to define the realm. The computing
device may receive
this selection in a variety of ways. As one possibility, the computing device
may present a GUI
through which a user may provide a user input or series of user inputs that
comprise a selection of
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one or more nodes or links. To illustrate one example of this, FIG. 13 depicts
an example snapshot
1300 of a GUI that displays an object selection panel 1302, a realm navigation
panel 1304, and a
network display location 1306. As depicted, the object selection panel 1302
may list the various
nodes and links of the entire semantic network. To select one or more of the
nodes or links of the
semantic network, the computing device may provide the ability to click on a
text listing of one
of the nodes or links and drag that text listing to the realm navigation panel
1304 to thus select
that node or link for inclusion into the realm. For instance, to select the
"Shipper" node for
inclusion into the realm being created, a user may click on the "Shipper" node
text entry and
corresponding icon listed in the object selection panel 1302 and drag it over
into the realm
navigation panel 1304. Likewise, to select the "Orders" link for inclusion
into the realm being
created, a user may click on the "Orders" link text entry and corresponding
icon listed in the object
selection panel 1302 and drag it over into the realm navigation panel 1304.
Other ways of
providing a user input to indicate a selection of one or more nodes or links
of a semantic network
is possible as well.
[179] In some embodiments, when a link (such as "Orders" link) is selected
for inclusion
in a realm (e.g., by a user dragging and dropping a text entry and
corresponding icon of a link
from the object selection panel 1302 to the realm navigation panel 1304), the
computing device
also includes in the realm being created any nodes that are associated by the
selected link. For
instance, in the example semantic network depicted in FIG. 13, if a user
selected the "Employee
Location" link (e.g., by dragging and dropping a text entry and corresponding
icon of the
"Employee Location" link from the object selection panel 1302 to the realm
navigation panel
1304), the computing device may also populate in the realm navigation panel
1304 the
"Employee" and "Address" nodes, thus selecting these nodes for inclusion into
the realm being
created as well.
[180] As also depicted in FIG. 13, the computing device may provide a
visualization to
help preview what nodes and links have been selected for inclusion into the
realm being created.
For instance, as depicted in display location 1306, the computing device may
display the
"Employee Location," "Employee Territories," and "Orders" links as smaller
circles and may also
display the "Employee," "Address," "Territory," "Product," "Customer,"
"Shipper," "Order
Date," "Ship Date," "Required Date," and "Vendor" nodes as each of these nodes
are associated
with one or more of the selected links. As depicted, this realm includes just
a subset of the nodes
and links of the full semantic network (e.g., as depicted in FIG. 4). The
computing device may
provide other visualizations to help preview and/or visualize the realm as
well. Notably,
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[181] Turning next to FIG. 11B, this figure presents a flow diagram 1120
depicting
another example of a process for engaging in data security operations for
semantic networks, and
in particular, for establishing and applying security profiles for instances
of a semantic network,
including realms. As depicted, this process may generally involve the
following operations: (i) at
block 1122, a computing device may receive an indication of a user for which
to apply a security
profile, (ii) at block 1124, the computing device may receive a section of a
permission profile to
apply for the user, (iii) at block 1126, the computing device may receive a
selection of a semantic
network instance, (iv) at blocks 1128 and 1130, the computing device may
either or both receive
a selection of semantic context information to block for the user or receive a
selection of semantic
context information to selectively filter for the user, and (v) at block 1132,
the computing device
may thereafter present a visualization of the semantic network to the user
implementing either or
both of the blocked context information or the filtered context information.
Each of these
operations will now be discussed in further detail with reference, in some
cases, to example
snapshots of GUIs that may facilitate some or all of the disclosed
functionality.
[182] Turning first to block 1122, the computing device may first receive
an indication
of a user for which to create and apply a security profile. To facilitate
receive this indication, the
computing device may present via a GUI one or more screens though which a user
may provide
various user inputs in order to provide the computing system with an
indication of a user for which
to create and apply a security profile. To illustrate one example of this,
FIG. 14A depicts an
example snapshot 1400 of an admin tool that presents a GUI through which a
user can provide
one or more user inputs that facilitate the computing device establishing and
applying security
profiles for instances of a semantic network, including realms. As depicted,
the GUI may provide
a user information area 1402 within which a user may provide various user
inputs to provide the
computing device with an indication of a user for which to create and apply a
security profile. For
example, as depicted, the user information area 1402 may include a location
for receiving a user
input to provide a user name and locations for receiving other information
about the user, such as
a last name, first name, email address, cell phone, etc. Other ways for
receiving an indication of a
user for which to create and apply a security profile may be possible as well.
[183] Returning to FIG. 11B at block 1124, the computing device may next
receive a
selection of a permission profile to apply for the user. A permission profile
may define what a
user can and cannot do with respect to modifying various aspects of a semantic
network or a realm
of a semantic network. For instance, one type of permission profile may be a
"user" profile,
whereby a user granted with a "user" permission profile would only be able to
view information
contained within the semantic network but may not be able to modify any
information, such as by
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creating new realms, or creating or modifying nodes, links, or properties.
Another type of
permission profile may be a "super user" profile, whereby a user granted with
this profile would
be able to view information contained within the semantic network and also
modify insights
related to the information, and perhaps other aspect about the semantic
network, such as creating
or modifying realms and/or nodes, like, or properties. And yet another type of
permission profile
may be an "administrator" profile, whereby a user granted with this profile
would be able to view
and modify information contained within the semantic network as well as grant
and modify
permissions for other users. Finally, yet another type of permissions profile
may be a specific
group profile, whereby a user granted with a specific group profile may have
one of the various
types of permissions profiles discussed above (e.g., a user, super user, or
administrator profile),
perhaps with other individual security settings, the establishment of which is
described further
herein.
[184] The computing device may receive a selection of a permission profile
to apply for
a user in a variety of ways. As one possibility, the computing device may
present a GUI, such as
that depicted in snapshot 1400 of FIG. 14A, via which a user may provide a
user input selecting
one of a set of available user profiles to apply for the selected user. For
instance, the GUI may
include a drop-down list or the like that lists the available permission
profiles. A user may then
select one of the permissions profiles, through a mouse click or touchscreen
tap, or the like, to
thereby select one of these profiles. The computing device may receive a
selection of a permission
profile in other ways as well.
[185] Next at block 1126, the computing device may receive a selection of a
semantic
network instance. One type of semantic network instance may be an entire
semantic network, such
as a semantic network that may have been created for an organization, which
may include each
and every node, link, and property of the semantic network. Another type of
semantic network
instance may be just a subset of an entire semantic network, such as a realm,
described above. To
facilitate receiving a section of a semantic network instance, the computing
device may present
via a GUI, one or more drop-down lists that include the semantic network
instances (including
realms) that are available for selection. FIG. 14B depicts an example snapshot
1410 of a GUI that
depicts an example drop-down list that 1404 lists example semantic network
instances for
selection. Through this list, for instance, a user may select one or more of
the semantic network
instances listed, through a mouse click or touchscreen tap or the like, and
thereby provide the
computing device with a selection of a semantic network instance. The
computing device may
receive a section of a semantic network instance in other ways as well.
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[186] Next at block 1128, the computing device may receive a selection of
semantic
context information to block for the user. The computing device may ultimately
present
visualizations for the selected user and exclude from these visualizations any
semantic information
that has been blocked. For instance, the computing device may receive a
selection of one or more
nodes to block. In this case, when the computing device presents a
visualization related to the
semantic network (e.g., a visualization of the semantic network in a web-like
structure or a
visualization that displays data related to the underlying data records that
have been assigned to
nodes and links of the semantic network, where such data may be in the form of
a data table,
graph, or some other format outlining or summarizing the underlying data
records), the computing
device will omit from the visualization any data related the blocked node(s).
For instance, where
the visualization is a web-like structure illustrating the semantic network,
the computing device
will omit from this visualization any depiction of the blocked node(s). In
another example, where
the visualization displays data related to the underlying data records that
have been assigned to
nodes and links of the semantic network, such as a data table, graph, or some
other format
outlining or summarizing the underlying data records, the computing device
will omit from the
visualization any underlying data records that have been assigned to a blocked
node, and any
insights defined for the semantic network that contain or are related to the
data records assigned
to a blocked node.
[187] In another example, the computing device may receive a selection of
one or more
properties to block. In this case, the computing device may, like described
above, present
visualizations related to the semantic network that omit certain data, but the
computing device
may omit data on a more granular level. For instance, if the computing device
receives a selection
of one or more properties of one or more nodes to block, then the computing
device may present
visualizations for the selected user and exclude from these visualizations any
data related to the
blocked properties of the nodes, but include any data related to the other
properties of the nodes.
By way of example, if the computing device receives a selection of the
property "Last Name" of
the node "Employees" to block, then for any visualizations that the computing
device ultimately
presents to the given user, the computing device may omit any data related to
the "Last Name"
property of the "Employee" node (such as omitting from the visualization any
employee last
names) but including in the visualization the other properties of the
"Employee" node, such as
"First Name" and perhaps an "Employee ID," (assuming the computing device has
not also
received selections to block these properties as well), among other possible
properties.
[188] In some embodiments, the computing device may receive a selection of
semantic
context information to block for the user on a less granular level. In one
example of this, the
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computing device may receive a selection of an entire semantic data type to
block for the user.
For instance, if the computing device receives a selection of an "Email
Address" semantic data
type to block for the user, then for any visualizations that the computing
device ultimately presents
to the given user, the computing device may omit any data that is assigned to
the "Email Address"
semantic data type no matter what node, property, or link the within which the
data happens to be
assigned.
[189] To facilitate receiving a selection of semantic context information
to block for the
user, the computing device may present via a GUI one or more graphical
locations within which
the computing device may receive user inputs to select semantic context
information to block for
the user. As depicted in FIG. 15A, for instance, snapshot 1500 depicts a GUI
that displays a
graphical location 1502 within which a user can select nodes of the semantic
network to block
and/or select properties of certain nodes of the semantic network to block. As
specifically depicted
in graphical location 1502, for instance, a user may have selected an "Agency"
node of a semantic
network to block. In this example, when the computing device ultimately
depicts a visualization
of the semantic network for this user, the computing device will omit from
this visualization any
depiction of the "Agency" node and/or any underlying data records that have
been assigned to the
"Agency" node, depending on the type of visualization, including any insights
that may have been
created that include data assigned to the "Agency" node
[190] Turning next to snapshot 1510 in FIG. 15B, this snapshot depicts a
GUI that
displays a similar graphical location 1512 within which a user can also select
properties of a node
or link of the semantic network to block. As specifically depicted in
graphical location 1512, for
instance, a user may have selected an "Agent Activity BusinessProcess" link
(which may, for
example, link an "Agent Activity" node and a "Business Process" node of the
semantic network),
and the user may have also selected a "TransactionPremiumAmt" property of the
selected link to
block. In this example, when the computing device ultimately depicts a
visualization of the
semantic network for this user, the computing device will omit from this
visualization any
depiction of the "TransactionPremiumAmt" property of the "Agent Activity
BusinessProcess"
link and/or any underlying data records that have been assigned to this
property, depending on the
type of visualization. Other ways to facilitate receiving a selection of
semantic context information
to block for the user are possible as well.
[191] Turning next to block 1130 in FIG. 11B, the computing device may
receive a
selection of semantic context information to selectively filter for the user.
As explained, the
computing device may ultimately present visualizations for the selected user.
However, if the
computing device has received a selection of semantic context information to
filter, then the
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computing device may include in the visualization semantic information data
that conforms to the
filter but exclude from these visualizations any semantic information that
does not conform to the
filter.
[192] For instance, the computing device may receive a selection of one or
more nodes
and/or properties to selectively filter. In this case, when the computing
device presents a
visualization related to the semantic network (e.g., a visualization of the
semantic network in a
web-like structure or a visualization that displays data related to the
underlying data records that
have been assigned to nodes and links of the semantic network, where such data
may be in the
form of a data table, graph, or some other format outlining or summarizing the
underlying data
records), the computing device will include in the visualization only data
related to the one or
more nodes and/or properties that have been selected for the filter and
exclude from the
visualization data not related to the selectively-filtered nodes. For
instance, where the visualization
is a web-like structure illustrating the semantic network, the computing
device will only include
a depiction of the selected nodes or properties and will omit from this
visualization any depiction
of the other nodes or properties. In another example, where the visualization
displays data related
to the underlying data records that have been assigned to nodes and links of
the semantic network,
such as a data table, graph, or some other format outlining or summarizing the
underlying data
records, the computing device will only display data related to the selected
nodes or properties
and will omit from the visualization any underlying data records that are
related to other nodes or
properties.
[193] In another example, the computing device may receive a selection of
one or more
nodes, properties, and underlying data to filter. In this case, the computing
device may, like
described above, present visualizations related to the semantic network that
omit certain data, but
the computing device may include other those data records that match or relate
to the selected
underlying data records for the selected property of the selected node. For
instance, if the
computing device receives a selection of one or more data records of a
particular property of a
particular node, then the computing device may present visualizations for the
selected user that
include the underlying data records or data related to the underlying data
records, and exclude
from these visualizations any other data records. By way of example, if the
computing device
receives a selection of the "John Smith" underlying data record for the
"Employee Name" property
for the "Employee" node, then for any visualizations that the computing device
ultimately presents
to the given user, the computing device may only include in these
visualizations data records that
are or are related to the "John Smith" employee and will omit from the
visualization other data
records that are or that relate to other employees.
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[194] In some embodiments, the computing device may receive a selection of
semantic
context information to selectively filter for the user on a less granular
level. In one example of
this, the computing device may receive a selection of an entire semantic data
type to selectively
filter for the user. For instance, if the computing device receives a
selection of an "Email Address"
semantic data type to filter for the user, then for any visualizations that
the computing device
ultimately presents to the given user, the computing device may include in
these visualizations
only such data that is assigned to the "Email Address" semantic data type no
matter what node,
property, or link the within which the data happens to be assigned related to
the "Email Address"
semantic data type and omit from the visualization any data.
[195] To facilitate receiving a selection of semantic context information
to block for the
user, the computing device may present via a GUI one or more graphical
locations within which
the computing device may receive user inputs to select semantic context
information to selectively
filter for the user. As depicted in FIG. 16A, for instance, snapshot 1600
depicts a GUI that displays
a graphical location 1602 within which a user can select a node, property,
and/or underlying data
records assigned to the property and node to selectively filter for the user.
As specifically depicted
in graphical location 1602, for instance, a user may have selected the
"Agency" node of a semantic
network, and the "BrokerPartyID" property of that node. Further, the user may
have selected
underlying data records of the "BrokerPartyID" property that match data values
of "84" and "87."
In this example, when the computing device ultimately depicts a visualization
of the semantic
network for this user, the computing device will only include in this
visualization underlying data
related to these selected "BrokerPartyID" values. Depending on the embodiment,
including in this
visualization underlying data related to these selected "BrokerPartyID" values
may mean that the
computing device would also include in the visualization underlying data from
other nodes and
other properties of the semantic network, so long as this other data is
related to the selected
"BrokerPartyID" values. For instance, if another node, say, a "Sales" node,
contained data related
to sales made by particular brokers, including for instance brokers
represented by the selected
"BrokerPartyID" values, then the computing device may also include in the
visualization
underlying data assigned to these sales (e.g., data related to sales made by
the brokers represented
by these selected "BrokerPartyID" values).
[196] FIG. 16B depicts another snapshot 1610 of a GUI that includes a
graphical location
1612 that depicts various filters that have been applied for a particular
user. For instance, as
depicted, the computing device may have received multiple selections of
semantic context
information to selectively filter for the user. As depicted in this particular
example, the computing
device may have received a selection of data values "84" and "87" from the
"BrokerPartyID"
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property of the "Agency" node and a selection of a data value "CN" from the
"POLBusinessType"
property of the "LOB" node. In practice, the computing device may receive many
different
selections of semantic context information to selectively filter for a given
user. When a computing
device receives multiple selections of semantic context information to filter
for a given user, such
as is depicted in graphical location 1612 in FIG. 16B, the computing device
may include in a
visualization underlying data so long as the underlying data relates to one
(or more) of the selected
semantic context information. Other ways to depict semantic context
information that has been
selectively filtered for a given user and other ways to facilitate receiving a
selection of semantic
context information to selectively filter for a given user are possible as
well.
[197] In embodiments in which, at block 1126, the computing device received
a selection
of a realm as the selection of a semantic network instance, then the computing
device may engage
in additional functionality in connection with the functionality described
above with respect to
block 1130. For instance, responsive to receiving a selection of a realm as
the selection of the
semantic network instance, the computing device may retrieve from data storage
the node that
was defined as the primary node during creation of the realm (e.g., as
described above with respect
to block 1104 (FIG. 11A). Further, the computing device may responsively
prompt the user (via
a GUI) to select one or more underlying data records assigned to a property of
this primary node
to selectively filter for the user, similar to the procedure described above
with respect to block
1130.
[198] For instance, in an example in which an "Employee" realm has its
primary node
defined to be the "Employee" node, then the computing device may ultimately
prompt the user
(via a GUI) to select one or more underlying data records assigned to the
"Employee" node (e.g.,
data records that represent individual employees) to selectively filter for
the user. In this way,
when the computing device presents to the user a visualization related to the
"Employee" realm,
the computing device may include in this visualization only those underlying
data records (e.g.,
data records related one or more employees) that are contained within the
nodes and links selected
for inclusion in the realm (e.g., as described above with respect to block
1106 (FIG. 11A)) and
that also relate to the underlying data records of the primary node selected
during the
aforementioned step. In this way, a realm (e.g., an "Employee" realm) can be
designed to include
only those nodes and links of the semantic network that include data that
ought to be viewed by
certain users (e.g. employees) and can be further designed to limit which data
records are
accessible depending on which user (e.g., which employee) is viewing the
visualization related to
the realm.
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[199] Finally, returning to FIG. 11B, at block 1132 the computing device
may present to
the user a visualization of the semantic network implementing either or both
of the blocked context
information and/or the filtered context information. As indicated above, if
the computing device
received a selection of semantic context information to block, then the
computing device may
present a visualization related to the semantic network (e.g., a visualization
of the semantic
network in a web-like structure or a visualization that displays data related
to the underlying data
records that have been assigned to nodes and links of the semantic network,
where such data may
be in the form of a data table, graph, or some other format outlining or
summarizing the underlying
data records) but omit from the visualization any data related the blocked
semantic context
information. As also described above, if the computing device received a
selection of semantic
context information to selectively filter, then the computing device may
present a visualization
that displays data related to the underlying data records that have been
assigned to nodes and links
of the semantic network, where such data may be in the form of a data table,
graph, or some other
format outlining or summarizing the underlying data records), including in
this visualization only
data related to the one or more nodes and/or properties that have been
selected for the filter and
exclude from the visualization data not related to the selectively-filtered
nodes. Other ways to
present to the user a visualization of the semantic network implementing
either or both of the
blocked context information and/or the filtered context information are
possible as well.
VI. CONCLUSION
[200] Example embodiments of the disclosed innovations have been described
above.
Those skilled in the art will understand, however, that changes and
modifications may be made to
the embodiments described without departing from the true scope and spirit of
the present
invention, which will be defined by the claims.
[201] Further, to the extent that examples described herein involve
operations performed
or initiated by actors, such as "humans," "operators," "users" or other
entities, this is for purposes
of example and explanation only. The claims should not be construed as
requiring action by such
actors unless explicitly recited in the claim language.
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