Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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METADATA MODELLING FOR REPORTING
Field of the Invention
The present invention relates generally to a metadata model, and more
particularly
to a metadata model used in a business intelligence and reporting system that
accesses
one or more data stores including relational databases.
Background of the Invention
Currently, information reporting systems use predefined query techniques to
hide
the complexity of Structured Query Language (SQL) and relational databases.
Typically,
members of Management Information System (MIS) staff build a database solution
by
creating user-dedicated tables, relational views or predefined SQL queries.
These are then
made available to users by means of menus or similar techniques. In such
systems, if end-
users want to change the purpose of a query they usually ask the MIS stafFto
program
another query. Alternatively the user may program the SQL query or command
themselves. However, the syntax of non-procedural languages (in particular
SQL) is
complex, and typically, the data structure is not expressed in terms of the
users' everyday
work. Relational databases store information as well as metadata (i.e. data
describing the
data organisation), such as tables, columns, keys, indices, and their
structure and design.
Although suited to the overall needs of the customer organisation, these
databases will
likely contain much that is not of interest to a particular user. In addition,
although a
query may be syntactically correct, its results may not be what are expected,
due to the
inherent complexity of a large-scale database. Indeed, the results may be
totally
meaningless.
For these and other reasons modeling tools are often used that allow
conceptual
modeling of databases. These tools provide a layer on top of the database, and
allow the
underlying database to be accessed in terms that are more relevant to a
particular end
application. Such modeling tools include "Impromptu", "Transformer", and
"Architect"
by Cognos Incorporated.
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US Patent 6,662,188 "Metadata model", Rasmussen, et al, issued December 9,
2003, hereby incorporated by reference, describes a three level abstraction
model for use
in such environments. This is briefly described with reference to Figure 2.
The lowest level in the database abstraction is the internal level 1. In the
internal
level 1, the database is viewed as a collection of files organized according
to an internal
data organization. The internal data organization may be anyone of several
possible
internal data organizations, such as B+-tree data organization and relational
data
organization.
The middle level in the database abstraction is the conceptual level 2. In the
conceptual level 2, the database is viewed at an abstract level. The user of
the conceptual
level 2 is thus shielded from the internal storage details of the database
viewed at the
internal level 1.
The highest level in the database abstraction is the external level 3. In the
external
level 3, each group of users has their own perception or view of the database.
Each view
is derived from the conceptual level 2 and is designed to meet the needs of a
particular
group of users. To ensure privacy and security of data, each group of users
only has
access to the data specified by its particular view for the group.
The mapping between the three levels of database abstraction is the task of
the
Database Management System (DBMS). When the data structure or file
organization of
the database is changed, the internal level 1 is also changed. When changes to
the internal
level 1 do not affect the conceptual level 2 and external level 3, the DBMS is
said to
provide for physical data independence. When changes to the conceptual level 2
do not
affect the external level 3, the DBMS is said to provide for logical data
independence.
Typical DBMSs use a data model to describe the data and its structure, data
relationships, and data constraints in the database. Some data models provide
a set of
operators that are used to update and query the database. DBMSs may be
classified as
either record-based systems or object-based systems. Both types of DBMSs use a
data
model to describe databases at the conceptual level 2 and external level 3
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Data models may also be called metadata models as they store metadata, i.e.,
data
about (or describing) data in databases: Three main existing data models used
in record
based systems are the relational model, the network model and the hierarchical
model.
In the relational model, data is represented as a collection of relations. To
a large
extent, each relation can be thought of as a table. A typical relational
database contains
catalogues, each catalogue contains schemas, and each schema contains tables,
views,
stored procedures and synonyms. Each table has columns, keys and indexes. A
lcey is a
set of columns whose composite value is distinct for all rows. No proper
subset of the key
is allowed to have this property. A table may have several possible keys. Data
at the
conceptual level 2 is represented as a collection of interrelated tables. The
tables are
normalized so as to minimize data redundancy and update anomalies. 'The
relational
model is a logical data structure based on a set of tables having common keys
that allow
the relationships between data items to be defined without considering the
physical
database organization.
A known high level conceptual data model is the Entity-Relationship (ER)
model.
In an ER model, data is described as entities, attributes and relationships.
An entity is
anything about which data can be stored. Each entity has a set of properties,
called
attributes, which describe the entity. A relationship is an association
between entities. For
example, a professor entity may be described by its name, age, and salary and
can be
associated with a department entity by the relationship "works for". Existing
information
systems use business intelligence tools or client applications that provide
data
warehousing and business decision making and data analysis support services
using a
data model. In a typical information system, a business intelligence tool is
conceptually
provided on the top of a data model, and underneath of the data model is a
database. The
data model of existing information systems typically has layers corresponding
to the
external level 3 and the internal level 1.
Existing data models are used for the conceptual design of databases. When a
system designer constructs an information system, the designer starts from a
higher
abstraction level 3 and moves down to a lower abstraction level 1, as
symbolized in
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Figure 2 by arrows. That is, the system designer first performs logical
design. At the
logical design stage, the designer considers entities of interest to the
system users and
identifies, at an abstract level, information to be recorded about entities.
The designer
then determines the conceptual scheme, i.e., the external level 3 and/or
conceptual level 2
of a data model. After the logical design is completed, the designer next
performs
physical design. At the physical design stage, the designer decides how the
data is to be
represented in a database. The designer then creates the corresponding storage
scheme,
i.e., the structure of a database, and provides mapping between the internal
level 1 of the
data model and the database.
Existing business intelligence tools typically provide a different paradigm
for
retrieving and delivering information from a database. Accordingly, it is
difficult to share
information in the database among different business intelligence tools.
It is common that in a single organization, each group of users has its own
established information system hat uses its corresponding database. Thus, a
single
organization often has multiple databases. Those databases often contain
certain types of
information that are useful for multiple groups of users. Such types of
information may
include information about business concepts, data retrieval, and user limits
and
privileges. However, each information system is designed and constructed in
accordance
with specific needs of the group, and may use a different business
intelligence tool from
others. These differences in the information systems and business intelligence
tools used
do not allow sharing the information already existing in the databases among
multiple
groups of users.
The previous invention, "Metadata Model - Rasmussen, et al", describes a data
model or metadata model that realizes the three abstraction levels and
provides
information that can be shared by multiple users who use those different
business
intelligence tools or client applications. It also provides for a metadata
model to contain
model objects representing one or more data sources. The data sources contain
tables
having columns. The metadata model comprises a data access layer, a business
layer and
a package layer. The data access layer contains data access model objects. The
data
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access model objects include table objects that describe definitions of the
tables contained
in the data sources, and column objects that describe definitions of the
columns of the
tables contained in the data sources. The business layer contains business
model objects.
The business model objects include entities that are constructed based on the
table objects
in the data access layer, and attributes that are constructed based on the
column objects in
the data access layer. The package layer contains package model objects. The
package
model objects include a package model object that references a subset of the
business
model objects.
What is needed is a way to reduce the apparent complexity and inherent
learning
barrier to the use of the earlier invention.
Brief Summary of the Invention
The present invention introduces a method and system that reduces the apparent
complexity faced by a user when confronted by this modeling environment.
In one aspect the invention provides for a method for use in a computer-based
business intelligence system having a client application receiving user
inputs, comprising
importing a database using a database schema, defining model objects within a
query
layer that are directly related to the imported database, creating one or more
packages
containing references to appropriate query objects, receiving inputs from a
user to select
appropriate combinations of model objects from the one or more packages,
selecting data
from the imported database using the selected appropriate combinations of
model objects,
and creating and presenting to a user a report incorporating the selected
data.
In a second aspect the invention provides for a computer-based business
intelligence system having a client application receiving user inputs,
comprising means
for importing a database using a database schema, means for defining model
objects
within a query layer that are directly related to the imported database, means
for creating
one or more packages containing references to appropriate query objects, means
for
receiving inputs from a user to select appropriate combinations of model
objects from the
one or more packages, means for selecting data from the imported database
using the
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selected appropriate combinations of model objects, and means for creating and
presenting to a user a report incorporating the selected data..
A number of advantages will be apparent from the following description and
associated claims. Relationships for the various query items are defined so
that they are
not necessarily based directly on the underlying database columns. Users are
provided
with a choice of mechanisms for defining the contents of a query subject in
terms of the
underlying database, or in terms of newly defined modelling objects, or in
combinations.
The definition of a query subject can be changed directly without reliance on
a separate
modelling layer. A package can to be a reference to a folder in the model, the
model
including a hierarchy. Any sub-tree of the model hierarchy may be published as
a
package for a particular audience.
Brief Description of Drawings
Embodiments of the invention will be described with reference to the following
drawings:
Figure l shows a general purpose computing environment in which the invention
may be practiced.
Figure 2 is a diagram showing an example of database abstractions;
Figure 3 is a diagram showing an example of a reporting system to which an
embodiment of the present invention may be applied.
Figure 4 is a diagram showing functions of the metadata exchange, metadata
model and transformations according to prior art.
Figure 5 illustrates the use of the metadata model by the query engine.
Figure 6 is a diagram showing functions of the metadata exchange, metadata
model and transformations according to an embodiment of the present invention.
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Detailed Description of the Invention
All embodiments of the invention may conveniently be implemented on any
general purpose computing platform, including one incorporated in a
client/server or
networked environment, such as that described with reference to Figure 1 in
which one or
more client computers 510, 511 are connected over a network 500 to a server
520, itself
connected to at least one data source 100. We now describe preferred
embodiments of the
present invention.
Although the abstraction levels described in the earlier invention are
necessary,
presenting them directly to modelers creates a substantial learning barrier to
those using
the tool because the levels are not related to how the users perceive the
data.
In order to make the model as useful as practicable, it is necessary to reduce
these
barriers to the point where the operation of the model is intuitive to users
that understand
the results expected from a reporting application, but who only have limited
knowledge
of the underlying database, or of database analysis techniques generally. In
this context,
users are the people (model designers and report authors) who develop report
outlines or
templates.
We therefore introduce new concepts, requiring (or rather resulting in) the
definition of related new terminology. This new terminology is described in
this new
context. The new terms are then used with the concepts to describe one
embodiment of
the invention. In doing so, the various simplifications, and rationalizations
made to the
original concept as described in the previous patent (Metadata Modeling) are
made clear.
Figure 3 illustrates a reporting system 4 to which an embodiment of the
present
invention is suitably applied. The reporting system 4 provides a single
administration
point for metadata that supports different business intelligence tools or
client
applications. Thus, it enables different business intelligence tools to
extract and interpret
data from various data sources in the same way.
The reporting system 4 includes common object services (COS) 5, a metadata
exchange 10, a metadata model 15, a metadata model transformer or
transformations 20,
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a user interface 25 arid a query engine 30. The fundamental objective of the
reporting
system 4 is to provide a rich business-oriented metadata model 15 that allows
the query
engine 30 to generate the best queries of which it is capable, and allows
users to build
queries, reports and cubes with the aid of the query engine 30 to obtain
desired reports
from underlying data sources. It is to this end that COS 5, metadata exchange
10 and
transformations 20 are provided.
Prior to describing the metadata model 15 and the transformations 20 in
detail,
each element of the reporting system 4 is briefly described, referring both to
Figure 3 and
Figure 4.
Referring first to Figure 3, COS 5 defines the framework for object
persistence.
Object persistence is the storage, administration and management of objects on
a physical
device and transfer of those objects to and from memory, as well as the
management of
those objects on the physical device. The double head arrow from COS 5 in
Figure 3
represents that COS 5 communicates with all other elements shown in Figure 3.
COS 5
performs functions such as creating new objects, storing them on disk,
deleting them,
copying them, moving them, handling change isolation (check-in, check-out) and
object
modeling.
Turning now to Figure 4, the metadata exchange 10 is used to obtain metadata
from external physical sources. Metadata is obtained from one or more external
sources
of metadata. External sources of metadata may be one or more data sources 100
and/or
one or more metadata sources 101. Data sources 100 contain physical data.
Examples of
data sources 100 include databases and files. Metadata sources 101 contain
descriptive
information about data sources. Metadata sources 101 axe also known as
metadata
repositories. Metadata repositories may be third party repositories. Metadata
sources
101 generally have underlying data sources 100 containing physical data. The
metadata
exchange 10 facilitates importation of metadata from external sources 100 and
1 O 1 into
the metadata model 15. Also, the metadata exchange 10 may facilitate
exportation of
metadata from the metadata model 15 to external metadata repositories.
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The metadata model 15 stores metadata about its underlying one or more data
sources 100. It is used to provide a common set of business-oriented
abstractions of the
underlying data sources 100. The metadata model 15 defines the objects that
are needed
to define client applications that users build. The metadata model 15 provides
three
layers to realize three levels of abstractions of data sources 100 as
described above
referring to Figure 2. The three layers are a physical layer or data access
layer 102, a
business layer 104 and a presentation layer or package layer 106.
Transformations 20 are used to complete the metadata model 15. For example,
when a database is introduced to the reporting system 4, metadata is imported
from the
database into the metadata model 15. Metadata may also be imported from one or
more
metadata repositories or other data sources 100. Sufficient metadata may be
imported
from a database that would build only a small number of the objects that would
actually
be needed to execute queries. However, if such metadata does not have good
mapping to
the metadata model 1 S, then the transformations 20 can be used to complete
the metadata
model 15.
Returning to Figure 3, the user interface 25 is layered on top of the metadata
model 15 as a basic maintenance facility. The user interface 25 provides users
with the
ability to browse through the metadata model 15 and rrianipulate the objects
defined
thereby.
The query engine 30 is responsible for taking the metadata model 15 and a
user's
request for information, and generating a query that can be executed against
the
underlying data sources 100, e.g., relational databases. The query engine 30
is basically
the reason for the existence of the rest of the blocks. The obj ective of the
query engine
is to function as efficiently as possible and to preserve the semantics of the
original
25 question. A user may ask a question that is not precise. The request may be
for
something from "customers" and something from "products". The query engine 30
then
determines from the metadata model 15 which relationship is used to relate
"customers"
and "products" to provide the user with information requested.
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The use of the metadata model 15 by the query engine 30 is further described
with
reference to Figure 5. A user uses a business intelligence tool or client
application (not
shown) to generate a user's request for information. Upon the receipt of the
user's
request, the client application generates an initial specification 35 based on
the request.
The specification 35 is generally not in a form that can be applied to the
data sources
directly. Using the information that is built in the metadata model 15; the
query engine
30 makes the specification 35 unambiguous and builds a query in terms of the
data access
layer 102 for the specification 35. This intermediate formulation of the query
is
subsequently translated into a data source specification language, which may
be
Structured Query Language (SQL): A query in a data source specification
language can
be executed on the data sources 100. Thus, the correct data 40 may be
obtained.
The rnetadata model 15 provides a unified and centralized modeling
environment,
and application program interfaces for business intelligence tools. The
metadata model
is composed of several layers, namely, a physical layer or data access layer
102, a
1 S business layer 104 and a presentation layer or package layer 106. These
layers
correspond to abstraction levels described earlier: with reference to Figure
1.
Referring now to Figure 6, one of the changes required to carry out
embodiments
of the present invention are shown and described. Specifically, the access
layer and
business layer of the previous system are combined and a new combined element,
the
query layer 110 is provided. This is possible because the entities and
attributes used in
defining the model have been replaced by the more powerful concepts of "query
subjects" and "query items". In other respects the operation is as described
with reference
to Figure 4 above.
In embodiments of the invention, when a database schema is imported, the
system
a set of unified database query subjects are created within the query layer
110. These
query subjects are directly tied to the underlying database, and are also
directly usable in
creating reports. These query subjects bring together all of the abstraction
and mapping
that the previous invention (Metadata Modeling) handled with 'connections'
between
physical and logical segments of the model. In contrast to a 'view', which is
defined by
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the database administrator DBA, a query subject is abstracted and separate
from the
underlying database, is applicable to and able to translate different kinds of
databases,
and provides a translation of the data and metadata into the terminology of
the user.
The query layer 110 includes mefadata that describes how to retrieve physical
data from data sources 100. It is used to formulate and refine queries against
the
underlying data sources 100. The underlying data sources 100 may be a single
or
multiple data sources, as described above. Examples of data sources 100
include
relational databases, such as Oracle, Sybase, DB2, SQL Server and Informix.
The query layer 110 contains model objects that directly describe actual
physical
data in the data sources 100 and their relationships. These model objects are
called query
subjects, and they in turn contain query items, which are attributes and
relate the columns
of the underlying databases. The query subjects may include, among other
things,
databases, catalogues, schemas, tables, files, columns, data access keys,
indexes and data
access joins, as well as data query language code such as SQL that assists in
the
transformation of the data. Each query subject has one or more columns. Data
access
joins exist between query subjects.
The query subjects in the query layer 1 l0 may be thought of as extended
metadata, created as a result of importing metadata from data sources 100 and
metadata
sources 101 provided by users. The information of some data access objects
maybe
available from the underlying data sources 100. Users can customize some
objects in the
data access layer 102 in order to create data access joins, i.e.,
relationships between
objects that were imported from various data sources.
The query layer 110 also describes the business view of the physical data in
the
underlying data sources 100. It is used to provide business abstractions of
the physical
data with which the query engine 30 can formulate queries against the
underlying data
sources 100.
The query layer 110 contains information encapsulated in the query subjects
and
query items that can be used to define in abstract terms the user's business
entities and
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their inter-relationships. These query subjects are reusable objects that
represent the
concepts and structure of the business to be used in business intelligence
environments.
They present a single unified business model, with direct relationships to the
underlying
databases, and can be related to physical data in a number of different data
sources 100.
'The query layer also includes business rules and display rules. As well as
query subjects
and query items, the query layer may include keys and joins. Since the query
subjects
within the query layer 110 have the ability to incorporate data query language
statements,
they may be used directly in creating reports.
In embodiments of the invention, the presentation of the business-oriented
objects
is simplified by having a package be simply a reference to a folder in the
model. Simple
applications are modelled as a hierarchical business structure. Any sub-tree
of this
structure may be published as a package for a particular audience. If there is
more than
one package; then each package is a subset of the model appropriate for a
particular
reporting application.
The various parts of the query layer 110 are closely related to the underlying
data
in that, for example, query subjects are related to tables in the data sources
100, and
query items correspond to columns in the underlying data sources 100. Business
joins
exist between query subjects. The new concepts of query subjects and query
items are not
totally analogous to their related tables and columns, since they permit an
extra level of
referencing, thereby providing powerful new benefits as will be obvious from
the
following description. Each business model object has a partner in the query
layer 110, .
i.e., a relationship exists between a table and an entity. The query subjects
also hold the
metadata representing the business concept. Query subjects are collections of
attributes.
Query items within the query layer 110 contain expressions related to columns
of
tables in the underlying database. A query item is usually directly related to
a single
column of the underlying data sources 100. For example, the query subject
"customer"
could have query items "customer name", "customer address", and the like. In
the
simplest case, all the query items of a query subject in the query layer 110
are related
one-to-one to the columns of a single table in the underlying data sources
100. However,
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the relationship is not always a one-to-one relationship. Also, a query item
may be
expressed as a calculation based on other query items, and constants, (and
columns). For
example, a query item may be a summary of data in other query items, e.g., a
total
amount of all the orders placed by customer.
Embodiments of the present invention thus provide a query layer that
describes,
for relevant data, how that data is to be retrieved or imported from the data
sources, and
also provides a 'business-oriented' view of that data. This is in contrast to
earlier systems,
where these aspects are separated into the data access model layer, and the
business layer.
During the importation process, the query subjects are converted to 'objects'
containing embedded data query language statements, such as SQL. During use,
such
objects may be inserted into existing data query language statements and the
embedded
data query language takes care of ensuring consistent syntax. A Graphical User
Tnterface
(GUI) that supports 'drag and drop' of data source tables and columns (not
objects) into
the data query language statements makes data query language editing easy. The
appropriate data query language syntax is created for these references and
inserted into
the data query language being edited. Further; a query subject is able to be
augmented
with query items and filters that use expressions based on model objects
(query items of
this query subject or others). In this case, the data query language for the
query subject
stored in the model is not affected. Rather, when a report is run that uses
this query
subject, the system generates data query language that combines the base data
query
language of the query subject with data query language created using such
augmented
query subjects.
Additional levels of abstraction can be built for different levels of
reporting by
creating model query subjects whose query items are simply references to other
query
items, which can be in either database queries or model queries. In other
words,
references to references can be used, to any arbitrary depth.
Embodiments of the invention use one or more of the following concepts:
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~ The creation of a single query layer to replace what previously required two
layers, viz. the data access layer and the business layer.
~ Within the query layer, access to the underlying databases is facilitated by
the
possibility of embedding of database access statements in a database query
object (known as a query subject).
~ Creation of objects appropriate to the user is facilitated by the inclusion
of
query items within the query subjects, and the ability to use query items and
query subjects freely within the query layer.
~ An extended form of database query language allows the inclusion of filters
(or filter expressions) and calculation building blocks capable of acting both
on query subjects and on query items contained in the query subjects. These
extensions to the database query language are not constrained by the
capabilities of the underlying databases and their related database query
languages.
Embodiments of the invention offer the following advantages:
The ability to define relationships for the various query items so that they
are
not necessarily based directly on the underlying database columns.
The ability to provide users with a choice of mechanisms for defining the
contents of a query subject in terms of the underlying database, or in terms
of
newly defined modelling objects, or in combinations thereof.
~ The ability to change the definition of a query subject directly without
reliance
on a separate modelling layer.
~ The ability of having a package to be a reference to a folder in the model,
the
model including a hierarchy.
~ The ability to allow any sub-tree of the model hierarchy to be published as
a
package for a particular audience.
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In embodiments of the invention, the concepts of entities (distinguishable
objects
to be represented in the database - usually by rows in relations) and
attributes (columns
or fields in a table) - are replaced bythe concepts of query subjects (i.e.
the subjects of
the relational query) and query items (i.e. particular attributes of a query
subject, or rather
a pointer or something that can be de-referenced as necessary to ultimately
provide those
attributes). These new concepts imply that an extra level of referencing is
possible,
providing powerful new benefits. In some embodiments, query items are defined
as
expressions. In other embodiments, both query items and filter expressions use
model
query items.
Embodiments of the invention have been found especially useful when the
changes required to define the query view based on the physical view are
relatively few.
The increasing use of data warehousing has resulted in more situations in
which this
circumstance is true.
