Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR MONITORING WEB CONTENT
TECHNICAL FIELD
[0001] The present disclosure relates generally to the monitoring of
dynamic
content. More specifically, it relates to a method and system for monitoring
content, such as web-pages, which are stored at a plurality of locations in a
location
set.
BACKGROUND
[0002] Monitoring web-page content and fetching web-page content may
be
useful in systems which index or classify such content. For example, search
engines, news aggregation services, and other indexing and classification
systems
may re-visit web-pages from time to time in order to determine whether content
associated with those web-pages has changed. Where content has changed, such
systems may update indexing and classification data .
[0003] Monitoring and fetching systems often visit web-pages in a
predetermined fixed order. This approach to monitoring and fetching may be
less
effective when monitoring highly dynamic web-pages and web-content. For
example, visiting web-pages in a predetermined fixed order may be inefficient
for
monitoring web-pages which are micro-blogs, such as Twitterm.
[0004] Thus there exists a need for improved systems and for
monitoring
content stored at a plurality of locations.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] Reference will now be made, by way of example, to the
accompanying
drawings which show an embodiment of the present application, and in which:
[0006] FIG. 1 shows a system diagram illustrating a possible
environment in
which embodiments of the present application may operate;
[0007] FIG. 2 shows a block diagram of a content monitoring system in
accordance with an embodiment of the present disclosure;
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[0008] FIG. 3 shows a block diagram of a content monitoring system in
accordance with a further embodiment of the present disclosure;
[0009] FIG. 4 shows a flowchart of a process for monitoring content
in
accordance with an embodiment of the present disclosure;
[0010] FIG. 5 shows a flowchart of a process for recognizing monitoring
content in accordance with a further embodiment of the present disclosure; and
[0011] FIG. 6 shows a flowchart of a process for recognizing
monitoring
content in accordance with another embodiment of the present disclosure.
[0012] Similar reference numerals are used in different figures to
denote
similar components.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0013] In one aspect the present disclosure provides a method of
monitoring
content stored at a plurality of locations in a location set. The method
comprises:
determining two or more historic attributes for a first feature associated
with each
location; for each location in the location set, determining a first predicted
attribute
for the first feature associated with that location based on the historic
attributes for
that first feature and that location; determining a monitoring schedule in
accordance with the first predicted attribute; and monitoring the content at
the
locations in the location set according to the monitoring schedule.
[0014] In another aspect, the present application provides a content
monitoring system for monitoring content stored at a plurality of locations in
a
location set. The system comprises a prediction component. The prediction
component is configured to determine two or more historic attributes for a
first
feature associated with each location. The prediction component is further
configured to, for each location in the location set, determine a first
predicted
attribute for the first feature associated with that location based on the
historic
attributes for that first feature and that location. The system further
comprises a
scheduling component configured to determine a monitoring schedule in
accordance
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with the first predicted attribute. The system further comprises a monitoring
component configured to monitor the content at the locations in the location
set
according to the monitoring schedule.
[0015] Other aspects and features of the present application will become
apparent to those ordinarily skilled in the art upon review of the following
description of specific embodiments of the application in conjunction with the
accompanying figures.
[0016] Reference is first made to FIG. 1, which illustrates a system
diagram of
a possible operating environment in which embodiments of the present
disclosure
may operate.
[0017] In the embodiment of FIG. 1, a content monitoring system 160 is
illustrated. The content monitoring system 160 is configured to monitor
content of
electronic documents 120a, 120b located at a plurality of locations 182, 184,
which
may be identified in a location set 180. That is, the content monitoring
system 160
is configured to monitor electronic documents 120a, 120b located at a set of
locations 182, 184 defined by a location set 180. The location set 180 is
stored in a
storage 190 which is accessible by the content monitoring system 160. The
storage
190 may, in some embodiments, be internal storage of the content monitoring
system 160. In other embodiments, the storage 190 may be external storage of
the content monitoring system 160, including, for example, network storage
accessible through a network 104.
[0018] The electronic documents 120a, 120b may vary over time. That is,
the
content of an electronic document 120a, 120b located at any given location
182,
184 may vary over time.
[0019] The electronic documents 120a, 120b may, in various embodiments,
be one or more of: Really Simple Syndication ("RSS") feeds or other cascaded
feeds, blogs, micro-blogs such as Twitter", on-line news sources, user-
generated
comments from web-pages, etc. Other types of electronic documents 120a, 120b
are also possible. By way of example and not limitation, the electronic
documents
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120a, 120b may be formatted in a Hyper-Text Markup Language ("HTML") format, a
plain-text format, or a portable document format ("PDF"). In some instances,
the
electronic documents 120a, 120b may be an image, such as a JPEG or Bitmap
image. Other document formats are also possible.
[0020] The electronic documents 120a, 120b may be located at associated
locations 182, 184 on a plurality of document servers 114a, 114b, which may be
accessible through a network 104, such as the Internet. In some embodiments,
the document servers 114 may be publicly and/or privately accessible web-pages
which may be identified by a unique Uniform Resource Locator ("URL"). In such
embodiments, the locations 182, 184 may be URLs.
[0021] The network 104 may be a public or private network, or a
combination
thereof. The network 104 may be comprised of a Wireless Wide Area Network
(WWAN), a Wireless Local Area Network (WLAN), the Internet, a Local Area
Network (LAN), or any combination of these network types. Other types of
networks are also possible and are contemplated by the present disclosure.
[0022] The location set 180 which defines the locations 182, 184 of
the
electronic documents 120a, 120b which are to be monitored may be stored on the
storage 190.
[0023] The storage 190 may include non-volatile memory such as, for
example, a Hard Disk Drive (HDD), Flash Memory, or other types of memory. In
some embodiments, the storage 190 may include a combination of different types
of memory.
[0024] The content monitoring system 160 may include functionality in
addition to the ability to monitor the content of electronic documents 120a,
120b
located at locations 182, 184. For example, as illustrated in FIG. 1, in some
embodiments, the content monitoring system 160 may be a document aggregation
system 150. The document aggregation system 150 may be configured to search
document servers 114a, 114b to locate and/or group electronic documents 120a,
120b which are related to a common subject matter.
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[0025] The electronic documents 120a, 120b may, in some embodiments,
be
news-related documents which contain information about recent, interesting,
topical and/or important events. In such cases, the document aggregation
system
150 may also be referred to as a news aggregation system. The news aggregation
system may be configured to locate and group electronic documents 120a, 120b
which are related to a common event or story.
[0026] The locations 182, 184 in the location set 180 may be
predefined fixed
locations. The locations 182, 184 may, in some embodiments, be specified, in
whole or in part by a user of the content monitoring system 160, such as, for
example, a system administrator.
[0027] In other embodiments, the location set may be dynamic. In such
embodiments, the content monitoring system 160 (which may be a document
aggregation system 150) may include a document search subsystem (not shown).
The document search subsystem (not shown) may be used by the document
aggregation system 150 to locate documents accessible through the network 104,
which may be located at locations which are not identified in the location set
180.
The document search subsystem may be configured to search document servers
114a, 114b based on a search algorithm in order to identify electronic
documents
120a, 120b matching a search criteria. By way of example, in some embodiments,
the search algorithm may provide for searching of websites (or other document
servers 114a, 114b) of a specific category using a search keyword or phrase.
For
example, the document search subsystem may be configured to search blogs,
micro
blogs, and/or online traditional news sources, etc.
[0028] The document search subsystem may, in some embodiments, rely
on
a third party search engine which may not be physically located within the
document aggregation system 150. For example, a publicly accessible search
engine, such as GoogleTM may be used.
[0029] If the document search subsystem 150 identifies electronic
documents
120a, 120b matching a search criteria, it may update the location set 180 to
include the locations of those identified documents. For example, in some
circumstances, the document search subsystem may search for electronic
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documents 120a, 120b which relate to a specific news item, such as a specific
event. If any such documents are located, the location set 180 may be updated
to
include the location 182, 184 of those electronic documents 120a, 120b in
order to
cause the content monitoring system 160 to monitor the content of the
documents
120a, 120b at those locations 182, 184.
[0030] In at least some embodiments, the document aggregation system
150
also includes a document classification subsystem (not shown) which associates
electronic documents 120a, 120b and/or the content therein with one or more
labels. For example, the document classification subsystem may associate one
or
more documents 120a, 120b with a phrase contained in the one or more document
120a, 120b. The label which is associated with the electronic document 120a,
120b
may be used to identify the subject matter of the electronic document 120a,
120b.
[0031] The document aggregation system 150 may include other
subsystems
not specifically described above. By way of example, the document aggregation
system 150 may, in some embodiments, include a ranking subsystem which ranks
documents 120a, 120b or the subject of documents 120a, 120b based on frequency
of use or frequency of occurrence. For example, the subjects of a plurality of
documents 120a, 120b may be ranked by determining the frequency of occurrence
of each label (such as a phrase) associated with documents 120a, 120b. The
rank
may indicate, in at least some embodiments, how topical the subject matter
associated with that label is.
[0032] In at least some embodiments, the document aggregation system
150
may include a web-interface subsystem (not shown) for automatically generating
web pages which provide links for accessing the documents 120a, 120b on the
document servers 114a, 114b and other information about the documents 120a,
120b. The other information may include a machine-generated summary of the
contents of the document, and the rank of the subject matter of the document
as
determined by the ranking subsystem (not shown). The web pages which are
generated by the web-interface subsystem may group documents 120a, 120b by
subject matter and/or by phrases which are used in the electronic documents
120a,
120b.
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[0033] By way of further example, other subsystems of the document
aggregation system 150 may also include a power subsystem for providing
electrical power to electrical components of the document aggregation system
150
and a communication subsystem for communicating with the document servers
114a, 114b through the network 104.
[0034] It will be appreciated that the content monitoring system 160
(and/or
the document aggregation system 150) may include more or less systems,
modules, subsystems and/or functions than are discussed herein. It will also
be
appreciated that the functions provided by any set of systems or subsystems
described above may be provided by a single system and that these functions
are
not, necessarily, logically or physically separated into different subsystems.
[0035] Furthermore, while FIG. 1 illustrates one possible embodiment
in
which the content monitoring system 160 may operate, (i.e. where the content
monitoring system 160 is a document aggregation system 150) it will be
appreciated that the content monitoring system 160 may be employed in any
system in which it may be useful to monitor the content of electronic
documents
120a, 120b located at locations 182, 184 of a location set 180.
[0036] Accordingly, the term content monitoring system 160, as used
herein,
is intended to include stand alone content monitoring systems which are not,
necessarily, part of a larger system, and also content monitoring sub-systems
which are part of a larger system (which may be the same or different than the
document aggregation system 150 of FIG. 1). The term content monitoring system
160 is, therefore, intended to include any systems in which the content
monitoring
methods described herein are included.
[0037] In at least some embodiments, the content monitoring system 160,
and/or the document aggregation system 150 may be implemented, in whole or in
part, by way of a processor 240 which is configured to execute software
modules
260 stored in memory 250. A block diagram of one such example content
monitoring system 160, is illustrated in FIG. 2.
[0038] In the embodiment of FIG. 2, the content monitoring system 160
includes a controller comprising one or more processor 240 which controls the
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overall operation of the content monitoring system 160. The content monitoring
system 160 also includes memory 250 which is connected to the processor 240
for
receiving and sending data to the processor 240. While the memory 250 is
illustrated as a single component, it will typically be comprised of multiple
memory
components of various types. For example, the memory 250 may include Random
Access Memory (RAM), Read Only Memory (ROM), a Hard Disk Drive (HDD), Flash
Memory, or other types of memory. It will be appreciated that each of the
various
memory types will be best suited for different purposes and applications.
[0039] The processor 240 may operate under stored program control and
may
execute software modules 260 stored on the memory 250. The software modules
260 may be comprised of, for example, a content monitoring module 280 which is
configured to monitor the content of one or more electronic documents 120a,
120b
(FIG. 1) located at locations 182, 184 identified in the location set 180.
[0040] The content monitoring module 280 may include a monitoring
component 234 which is configured to monitor electronic documents 120a, 120b
(FIG. 1) according to a monitoring schedule 202. The monitoring schedule 202
specifies the order in which the content of electronic documents 120a, 120b at
locations 182, 184 of the location set 180 are monitored.
[0041] The monitoring schedule 202 may be determined by a scheduling
component 234 of the content monitoring module 280. The monitoring schedule
202 may be stored in the storage 190 by the scheduling component 232 and
retrieved by the monitoring component 234. Methods of determining the
monitoring schedule 202 will be discussed in greater detail below.
[0042] The monitoring schedule 202 may, in at least some embodiments,
act
as a queue which lists locations 182, 184 in the order in which they are to be
monitored. For example, in at least some embodiments, the monitoring component
234 is configured to monitor the documents 120a, 120b at the locations 182,
184 in
the monitoring schedule 202 in the order in which they are listed in the
monitoring
schedule.
[0043] Monitoring electronic documents 120a, 120b may, in various
embodiments, include retrieving the electronic documents 120a, 120b from their
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respective locations 182, 184 and may also include saving the electronic
documents
120a, 120b to the storage 190. That is, the monitoring component 234 may, in
various embodiments, be configured to fetch the electronic documents 120a,
120b
from their respective locations 182, 184 and to save the electronic documents
120a, 120b to the storage 190. For example, the electronic documents 120a,
120b
may be saved in a fetched content 206 portion of the storage 190.
[0044] In at least some embodiments, monitoring electronic documents
120a,
120b may include monitoring documents referred to and/or linked to in the
electronic documents 120a, 120b located at the locations 182, 184 in the
location
set 180. For example, the document 120a, 120b located at a location 182, 184
in
the location set 180 may, in some embodiments, be a cascaded data object such
as
an RSS feed. In such cases, the monitoring component 234 may be configured to
visit locations referred to or linked in the document that is the RSS feed,
when
monitoring that document. That is, the monitoring component may visit
locations
referred to or linked to in an RSS document in order to retrieve and/or fetch
content from other documents located at the referred-to or linked-to
locations.
[0045] In at least some embodiments, the monitoring component 234 may
be
configured to perform a duplication checking analysis on fetched content 206
before
saving the content to the storage 190. The monitoring component 234 may
compare the fetched content with fetched content already saved to the storage
190. If the monitoring component 234 determines that the content has not
already
been saved to the storage 190, it may save the content to the storage 190.
Alternatively, if the monitoring component 234 determines that the content has
already been saved to the storage, it may not re-save the content to the
storage
190.
[0046] The monitoring component 234 may be further configured to
analyze
electronic documents 120a, 120b located at the locations 182, 184 of the
location
set 180 to determine one or more attributes associated with features of the
electronic documents 120a, 120b. Each attribute may be related to a feature of
the
electronic documents 120a, 120b at a specific point in time. In at least some
embodiments, the attribute may be related to a feature of the electronic
document
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120a, 120b at the point in time in which the electronic documents 120a, 120b
are
fetched from their respective locations 182, 184. The time which is related to
each
attribute is, generally, a time which has already passed. Thus, the attributes
may,
in at least some embodiments, be referred to as historic attributes. Since the
attributes are each related to one or more features of the electronic document
120a, 120b, the attributes may also be referred to as feature attributes 204.
[0047] The feature attributes 204 may be a value, quantifier, or
other
attribute associated with a feature of an associated electronic document 120a,
120b
at an associated point in time. That is, the feature attributes 204 serve to
quantify
features.
[0048] The features of the electronic documents 120a, 120b represent
information about the electronic document 120a, 120b which may be used to
determine how frequent the location 182, 184 associated with the document
120a,
120b will be monitored. That is, features are characteristics associated with
the
electronic document 120a, 120b which may be used in order to determine how
often the location 182, 184 of the document 120a, 120b should be revisited for
monitoring and/or how the monitoring of the document 120a, 120b should be
prioritized relative to the monitoring of other documents 120a, 120b.
[0049] The features may include one or more of: an indicator of
whether the
document at a location was updated or not updated since a last visit to that
same
location, an indicator of the age of the document (for example, the elapsed
time
since the last change to the document), a quantifier of the number of comments
associated with the electronic document 120a, 120b (for example, if the
electronic
document 120a, 120b is a web page which permits commenting, the comments
may be a feature and the number of comments may be a feature attribute),
and/or
a quantifier of the number of inlinks associated with the electronic document
120a,
120b.
[0050] Inlinks are links, such as hyper-text links, which point to
the electronic
document 120a, 120b. The number of inlinks is not determined from the document
120a, 120b itself, but rather, from examining other documents to determine
whether they link to the document 120a, 120b.
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[0051] The feature may also include a feature which is a link
analysis based
ranking associated with the electronic document 120a, 120b. For example, a
PageRankTM associated with an electronic document 120 may be a feature of that
electronic document 120a, 120b. The specific value or other quantifier of the
feature for each document 120a, 120b at an associated time is the feature
attribute
204 for that feature. For example, a specific PageRankTM value associated with
a
specific electronic document 120a, 120b at a specific point in time may be an
attribute of a PageRankTM feature for that electronic document 120a, 120b.
[0052] Other features apart from those specifically discussed above
are also
possible.
[0053] The feature attributes 204 which are determined by the
monitoring
component 234 may be saved to storage 190 associated with the content
monitoring system 160. In at least some embodiments, the feature attributes
204
may be saved in a features database in the storage 190. Each feature attribute
204 may be saved along with a time related to that feature attribute 204. That
is,
the feature attributes 204 may be saved in a time-series fashion. The time
may, in
at least some embodiments, be the time at which the feature attributes 204
were
observed or determined. In at least some embodiments, the time may be saved
using POSIX time convention. However, other time formats may also be used.
[0054] In at least some embodiments, the monitoring component 234 may be
configured to only record a finite number of values associated with each
feature for
each location 182, 184 in the location set 180. This finite number may be
defined
by a feature attribute threshold. Once the feature attribute threshold is met,
older
feature attributes 204 may be removed from storage 190 in order to make room
for
newer features attributes 204. For example, in some embodiments, the
monitoring
component 234 may be configured to record only the last k-feature attributes
204
associated with each feature for each location.
[0055] The storage 190 may, in some embodiments, be internal
storage of
the content monitoring system 160, such as internal memory of the content
monitoring system 160. In other embodiments, the storage 190 may be external
storage which is accessible by the content monitoring system 160. For example,
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the storage 190 may, in some embodiments, be network storage.
[0056] The content monitoring module 280 may also include a
prediction
component 230. As will be explained in greater detail below, the prediction
component 230 may be configured to, for each location 182, 184 in the location
set
180, determine a first predicted attribute for the first feature associated
with that
location based on the historic feature attributes 204 for that first feature
and that
location 182, 184. That is, in at least some embodiments, the prediction
component 230 may, for each location 182, 184 in the location set 180,
determine
a future attribute for a first feature associated with that location based on
historic
feature attributes 204 for that first feature and that location. The
prediction
component 230 may attempt to determine future attributes of features based on
previously observed attributes of that same feature.
[0057] For example, where the feature is an indicator of whether the
document was updated or not updated since a last visit, the prediction
component
230 may attempt to predict whether, at some future time, the document will be
updated or not since the last visit. Similarly, where the feature is an
indicator of
the age of the document (for example, the elapsed time since the last change
to
the document), the prediction component 230 may attempt to predict what the
age
of the document will be at some future time. Similarly, where the feature is a
quantifier of the number of comments associated with the electronic document
120a, 120b, the prediction component 230 may attempt to predict the number of
comments associated with the electronic document at some future time.
Similarly,
where the feature is a quantifier of the number of inlinks associated with the
electronic document 120a, 120b, the prediction component 230 may attempt to
predict the number of inlinks associated with the electronic document 120a,
120b
at some future time.
[0058] Similarly, where the feature is a link analysis based ranking
associated
with the electronic document 120a, 120b (such as PageRankTm), the prediction
component 230 may attempt to predict the link analysis based ranking
associated
with the electronic document 120a, 120b at some future time.
[0059] The prediction component 230 may, in at least some
embodiments,
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include a regression computation module which performs a regression analysis
on
historic attributes (also known as feature attributes 204) associated with a
feature
and a location in order to determine predicted attributes for that same
feature and
location.
[0060] It will be appreciated that the historic attributes may be taken at
times
that are irregular. That is, since monitoring does not occur in a fixed order,
the
time period between successive feature attributes for any location may be
variable.
Accordingly, a regression analysis which does not require fixed time intervals
may
be utilized by the prediction component 230. For example, in at least some
embodiments, a brown's double exponential smoothing method may be used. In
such embodiments, a predicted attribute for a feature and a location may be
determined according to the following formula:
X õ = (1¨ Võ) = X õ_.! +17Xn
where:
Võ = bõ+Võ
bõ = (1¨ ,
X0 = 0,
V0 =1¨ (1¨ a)" , and
Lo -11
q=
n +1
Where X, is the predicted attribute, a is a smoothing parameter, n is the
number of
historic attributes for the feature and location which are used to determine
the
predicted attribute, t is the time associated with a historic attribute (i.e.
tn is the
time for the nth historic attribute for that feature and that location). X,_,
is a last
predicted attribute, and X is a feature attribute. The smoothing parameter is
a
value which is, in at least some embodiments, between the range of zero (0) to
one
(1). In at least some embodiments, the smoothing parameter is approximately
0.1.
[0061] In other embodiments, an extended Holt's approach may be used
to
perform a regression analysis. In such embodiments, a linear regression step
may
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be performed to create a regression line using historic feature attributes
204. More
particularly, if we let So=A and To=B, where A is the intercept of the
regression line
at to and B is the slope of the linear regression line. The predicted
attribute can be
determined by iterating through the following steps:
Sõ,, = (1¨ aõ,1)=[Sõ+(t1¨ tõ)=Tõ1+
¨Sõ
Tn+1¨ (1¨ rn-1) = Tit 4- 711,1
t õ t õ
Where variable smoothing coefficients are given as:
aõ
aõ,i= ________________
aõ+ (1¨
r,
7õ + (1¨ , -1
where a E (0,1) is a smoothing constant for the level and yE (0,1) is a
smoothing
constant for the slope.
[0062] The predicted attribute may be calculated as:
X,+õ(t)= S,+ n.T,
[0063] In other embodiments, a linear regression method may be used
to
determine predicted attributes.
[0064] In at least some embodiments, predicted attributes for more
than one
feature may be determined for each location 182, 184. In such embodiments, the
prediction component 230 may, for each location 182, 184 in the location set
180,
gather the predicted attributes for more than one feature and compute a
performance metric value based on those predicted attributes. For example, in
at
least some embodiments, the prediction component 230 may apply a
predetermined function to the predicted attributes for multiple features in
order to
compute a performance metric value. By way of example and not limitation, each
feature may have a weighting value associated with that feature. The
performance
metric value may, in at least some embodiments, be calculated as the sum of
the
products of the predicted attribute of features and the weighting value
associated
with that feature. For example, in some embodiments, the multiple features may
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include the number of comments associated with a document (i.e. the first
feature)
and the number of in links associated with the document (i.e. the second
feature).
In such embodiments, the performance metric value may be calculated based on
both a predicted attribute related to the number of comments expected to be
associated with the document at some future time and a predicted attribute
related
to the number of inlink expected to link to the document at some future time.
[0065] The content monitoring module 280 may also include a
scheduling
component 232. The scheduling component 232 may determine a monitoring
schedule 202 based on the predicted attributes and/or the performance metric
values determined by the prediction component 230.
[0066] For example, the scheduling component 232 may schedule the
monitoring of the locations 182, 184 in the location set 180 based on the
predicted
attributes and/or the performance metric values; locations which have higher
predicted attributes and/or higher performance metric values may be placed
higher
on the monitoring schedule 202 (and thus monitored sooner) than locations with
relatively lower predicted attributes and/or lower performance metric values.
[0067] In at least some embodiments, the scheduling component 232 may
be
configured to increase the rank of a location in the monitoring schedule 202
if that
location becomes stale. For example, the rank of a location may be increased
based on the period of time which has elapsed since the location was last
monitored. The period of time may be measured, for example, in terms of the
number of fetching or monitoring operations which have occurred by the
monitoring
component 234 since the location was last monitored. In some embodiments, the
rank of a location in the monitoring schedule 202 may be increased by
increasing
the performance metric value associated with that location. For example, the
predicted performance metric could be incremented by a predetermined amount
for
every thousand fetching operations. It will be appreciated however, that a
thousand fetching operations is intended to be illustrative and that other
thresholds
may be used.
[0068] It will be appreciated that the division of functions between
components could, in some embodiments, be different than that specifically
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described above. That is, any functions provided by any one of either the
prediction component 230, scheduling component 232 and monitoring component
234, could be performed by another component, module, or system. For example,
any one or more of the components 230, 232, 234 or modules 280 may be
logically
or physically organized in a manner that is different from the manner
illustrated in
FIG. 2.
[0069] It will also be appreciated that, while the location set 180
and the
monitoring schedule 202 are depicted in FIG. 2 using separate blocks, in at
least
some embodiments, the location set 180 and the monitoring schedule 202 may be
a single element. For example, a single list of locations may serve as both a
location set 180 and a monitoring schedule 202. For example the order of the
listing of locations in the location set 180 may define the order of
monitoring.
[0070] Referring now to FIG. 3, a block diagram of a further example
of
content monitoring systems 160 is illustrated. In the example of FIG. 3, a
first
content monitoring system 360 and a second content monitoring system 362 are
connected to a common storage 190. The first content monitoring system 360 and
the second content monitoring system 362 may retrieve and update data which is
common to both content monitoring systems 360 and 362. For example, the first
content monitoring system 360 and the second content monitoring system 360 may
share fetched content 206, feature attributes 204, a monitoring schedule 202
and/or a location set 180. Due to the sharing of data, the capacity of the
system to
monitor documents may be increased simply by adding additional content
monitoring systems 160.
[0071] It will be appreciated that, while FIG. 3 illustrates an
example where
two content monitoring systems 160 are used in order to provide additional
capacity, in other embodiments, additional content monitoring systems 160
could
be used in order to provide greater capacity.
[0072] Referring now to FIG. 4, a process 400 for monitoring content
stored
at a plurality of locations 182, 184 (FIG. 1) in a location set 180 (FIG. 1)
is
illustrated in flowchart form. The process 400 includes steps or operations
which
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may be performed by the content monitoring system 160 of FIGs. 1 to 3. In at
least some embodiments, the content monitoring module 280 may be configured to
perform the steps or operations of the process 400 of FIG. 4. The steps or
operations of the process 400 of FIG. 4 may be performed by one or more of the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 of FIG. 2. That is, the content monitoring module 280, the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 may contain instructions for causing the processor 240 to
execute
the process 400 of FIG. 4.
[0073] First, at step 410, the monitoring component 234 of the content
monitoring module 280 may retrieve a monitoring schedule 206 (FIG. 2) from
storage 190 and may access a location 182, 184 in a location set 180 according
to
the monitoring schedule 202. The monitoring schedule 202 specifies the order
in
which the content of electronic documents 120a, 120b at locations 182, 184 of
the
location set 180 are monitored.
[0074] The monitoring schedule 202 may, in at least some embodiments,
act
as a queue which lists locations 182, 184 in the order in which they are to be
monitored. For example, in at least some embodiments, the monitoring component
234 will monitor the documents at the locations 182, 184 in the monitoring
schedule 202 in the order in which they are listed in the monitoring schedule
202.
In such embodiments, the location accessed at step 410 may be the location at
the
top of the queue.
[0075] The monitoring schedule 202 may, at least initially, be
randomly or
arbitrarily determined. For example, all of the locations 182, 184 in the
location set
180 may be added to the monitoring schedule 202 in a random or arbitrary
manner.
Other methods of initializing the monitoring schedule 202 are also possible.
As will
be explained in greater detail below, the monitoring schedule 202 will be
updated in
a manner which permits locations to be monitored in a dynamic manner. That is,
the monitoring schedule 202 is not simply a fixed schedule in which locations
are
always monitored in the same predetermined order. The order of monitoring will
vary as described below.
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[0076] Step 410 includes a step of retrieving the electronic document
120a,
120b at the location 182, 184 specified by the monitoring schedule 202. Step
410
may also include a step of saving the electronic documents 120a, 120b to the
storage 190. That is, the monitoring component 234 may, in various
embodiments,
be configured to fetch the electronic documents 120a, 120b from their
respective
locations 182, 184 and to save the electronic documents 120a, 120b to the
storage
190. For example, the electronic documents 120a, 120b may be saved in a
fetched
content 206 portion of the storage 190.
[0077] In at least some embodiments, monitoring electronic documents
120a,
120b at step 410 may include monitoring documents referred to and/or linked to
in
the electronic documents 120a, 120b located at the locations 182, 184 in the
location set 180. For example, the document 120a, 120b located at a location
182,
184 may, in some embodiments, be a cascaded data object such as an RSS feed.
In such cases, the monitoring component 234 may be configured to visit
locations
referred to or linked to in the document that is an RSS feed, when monitoring
that
document. That is, the monitoring component may visit locations referred to or
linked to in an RSS document in order to retrieve and/or fetch content from
other
documents located at the referred-to or linked-to locations.
[0078] In at least some embodiments, at step 410, the monitoring
component
234 may be configured to perform a duplication checking analysis on fetched
content 206 before saving the content to the storage 190. The monitoring
component 234 may compare the fetched content with fetched content already
saved to the storage 190. If the monitoring component 234 determines that the
content has not already been saved to the storage, it may save the content to
the
storage 190. Alternatively, if the monitoring component 234 determines that
the
content has already been saved to the storage, it may not re-save the content
to
the storage 190.
[0079] Next, at step 420, the monitoring component 234 may analyze
the
retrieved electronic documents 120a, 120b located at the location 182, 184
specified by the monitoring schedule 202 to determine one or more feature
attributes 204 associated with features of the electronic documents 120a,
120b.
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Each feature attribute 204 may be related to a feature of the electronic
documents
120a, 120b at a specific point in time. In at least some embodiments, the
feature
attribute 204 may be related to a feature of the electronic document 120a,
120b at
the point in time in which the electronic documents 120a, 120b are fetched
from
their respective locations 182, 184. The time which is related to each feature
attribute 204 is, generally, a time which has already passed. Thus, the
feature
attributes 204 may, in at least some embodiments, be referred to as historic
attributes.
[0080] Each feature attribute may be a value, quantifier, or other
attribute
associated with a feature of an associated electronic document 120a, 120b at
an
associated point in time. That is, the feature attributes 204 serve to
quantify
features. Each feature attribute 204 is associated with both a feature and a
location.
[0081] The features of the electronic documents 120a, 120b represent
information about the electronic document 120a, 120b which may be used to
determine how frequent the location 182, 184 associated with the document
120a,
120b will be monitored. That is, features are characteristics associated with
the
electronic document 120a, 120b which may be used in order to determine how
often the location 182, 184 of the document 120a, 120b should be revisited for
monitoring and/or how the monitoring of the document 120a, 120b should be
prioritized relative to the monitoring of other documents 120a, 120b.
[0082] The features may include one or more of: an indicator of
whether the
document was updated or not updated since a last visit, an indicator of the
age of
the document (for example, the elapsed time since the last change to the
document), a quantifier of the number of comments associated with the
electronic
document 120a, 120b (for example, in the electronic document 120a, 120b is a
web page which permits commenting, the comments may be a feature), and/or a
quantifier of the number of inlinks associated with the electronic document
120a,
120b.
[0083] Inlinks are links, such as hyper-text links, which direct to the
electronic document 120a, 120b. The number of inlinks is not determined from
the
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document itself, but rather, from examining other documents to determine
whether
they link to the document.
[0084] The features may also include a feature which is a link
analysis based
ranking associated with the electronic document. For example, a PageRankTM
associated with an electronic document 120 may be a feature of that electronic
document 120a, 120b. The specific value or other quantifier of the feature for
each
document 120a, 120b at an associated time is the attribute for that feature.
For
example, a specific PageRankTM value associated with a specific electronic
document
120a, 120b at a specific point in time may be a feature attribute of a
PageRank
feature for that electronic document 120a, 120b.
[0085] Other features apart from those specifically discussed above
are also
possible.
[0086] Next, at step 430, the feature attribute 204 which is
determined by
the monitoring component 234 may be saved to storage 190 associated with the
content monitoring system 160. In at least some embodiments, the feature
attributes 204 may be saved in a features database in the storage 190. The
feature
attributes 204 may be saved along with a time related to the feature
attributes 204.
That is, the feature attributes 204 may be saved in a time-series fashion. The
time
may, in at least some embodiments, be the time at which the feature attributes
204
were observed or determined. In at least some embodiments, the time may be
saved using POSIX time convention. However, other time formats may also be
used.
[0087] In at least some embodiments, the monitoring component 234 may
be
configured to only record a finite number of values associated with each
feature for
each location 182, 184 in the location set 180. This finite number may be
defined
by a feature attribute threshold. Once the feature attribute threshold is met,
older
feature attributes 204 may be removed from storage 190 in order to make room
for
the newer feature attributes. For example, in some embodiments, the monitoring
component 234 may record only the last k-feature attributes 204 associated
with
each feature for each location.
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[0088] Next, at step 440, the prediction component 230 may determine
a first
predicted attribute for the first feature associated with the location based
on the
historic feature attributes 204 for that first feature and that location.
[0089] That is, in at least some embodiments, the prediction
component 230
may determine a future attribute for a first feature associated with the
location
accessed in step 410 based on historic feature attributes 204 for that first
feature
and that location. The prediction component 230 may attempt to determine
future
attributes of features based on previously observed attributes of that same
feature.
[0090] The prediction component 230 may, in at least some
embodiments,
perform a regression analysis on historic attributes associated with a feature
and
the location accessed in step 410 in order to determine predicted attributes
for that
same feature and location.
[0091] In at least one embodiment, at step 440, a brown's double
exponential
smoothing method may be performed. In such embodiments, a predicted attribute
for a feature and a location may be determined according to the following
formula:
Xõ =(1¨Võ)=Xõ_, +17Xn
where:
V
V= _________________
"
bõ = (1¨
X0 = X0,
= 1 - (1 - a)" , and
Lo
q=
n+1
Where X,, is the predicted attribute, a is a smoothing parameter, n is the
number of
historic attributes for the feature and location which are used to determine
the
predicted attribute, t is the time associated with a historic attribute (i.e.
tn is the
time for the nth historic attribute for that feature and that location). X,_1
is a last
predicted attribute and Xn is a feature attribute The smoothing parameter is a
value
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which is, in at least some embodiments, between the range of zero (0) to one
(1).
In at least some embodiments, the smoothing parameter is approximately 0.1.
[0092] In other embodiments, an extended Holt's approach may be used
to
perform a regression analysis. In such embodiments, a linear regression step
may
be performed to create a regression line using historic feature attributes
204. More
particularly, if we let So=A and To=B, where A is the intercept of the
regression line
at to and B is the slope of the linear regression line. The predicted
attribute can be
determined by iterating through the following steps:
= (1¨ a,) [S, + (t1¨tõ)=Tõ1+ aõ,i = ynil
Tn+1¨ (1¨ Tri-1) = 1-;, rõ,1
t,I-t
õ õ
Where variable smoothing coefficients are given as:
aõ
aõ,i=
aõ + (1¨
r,
-
yõ+ (1¨ y),
where a E (0,1) is a smoothing constant for the level and yE (0,1) is a
smoothing
constant for the slope.
[0093] The predicted attribute may be calculated as:
X,+n(t)= +n .T,
[0094] In other embodiments, a linear regression method may be used
to
determine predicted attributes.
[0095] Next, at step 450, the monitoring component 232 may update the
monitoring schedule 202 based on the predicted attribute determined at step
440.
For example, the scheduling component 232 may, at step 450, schedule the
monitoring of the locations 182, 184 in the location set 180 based on the
predicted
attribute determined at step 440. In at least some embodiments, locations
which
have higher predicted attributes may be placed higher on the monitoring
schedule
202 (and thus monitored sooner) than locations with relatively lower predicted
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attributes.
[0096] The process 400 may then repeat itself so that the scheduling
and
monitoring of locations proceeds indefinitely, or until some predetermined
stop
condition is satisfied.
[0097] Referring now to FIG. 5, a further process 500 for monitoring
content
stored at a plurality of locations 182, 184 (FIG. 1) in a location set 180
(FIG. 1) is
illustrated in flowchart form. The process 500 includes steps or operations
which
may be performed by the content monitoring system 160 of FIGs. 1 to 3. In at
least some embodiments, the content monitoring module 280 may be configured to
perform the steps or operations of the process 500 of FIG. 5. The steps or
operations of the process 500 of FIG. 5 may be performed by one or more of the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 of FIG. 2. That is, the content monitoring module 280, the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 may contain instructions for causing the processor 240 to
execute
the process 500 of FIG. 5.
[0098] The process 500 of FIG. 5 is similar to the process 400 of
FIG. 4,
except in that, in the process 500 of FIG. 5, the scheduling is made based on
historic feature attributes 204 for more than one feature. Step 520 of FIG. 5
is
similar to step 420 of FIG. 4, except in that, at step 520 of FIG. 5, feature
attributes 204 for a plurality of features are determined. For example, in
some
embodiments, a feature attribute for a first feature and a feature attribute
for a
second feature may be determined.
[0099] The features may include one or more of: an indicator of
whether the
document was updated or not updated since a last visit, an indicator of the
age of
the document (for example, the elapsed time since the last change to the
document), a quantifier of the number of comments associated with the
electronic
document 120a, 120b (for example, in the electronic document 120a, 120b is a
web page which permits commenting, the comments may be a feature), and/or a
quantifier of the number of inlinks associated with the electronic document
120a,
120b. The feature may also include a feature which is a link analysis based
ranking
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associated with the electronic document 120a, 120b. For example, a PageRankTM
associated with an electronic document 120 may be a feature of that electronic
document 120a, 120b.
[00100] Other features apart from those specifically discussed above
are also
possible.
[00101] Similarly, step 530 of FIG. 5 is similar to step 430 of FIG. 4
except in
that, at step 530 of FIG. 5, feature attributes for multiple features
associated with a
location are stored. Similarly, step 540 of FIG. 5 is similar to step 440 of
FIG. 4
except in that, at step 540 predicted attributes for multiple features are
determined.
[00102] Next, at step 550, the prediction component 230 may, for the
location
accessed at step 410, gather predicted attributes for more than one feature
and
compute a performance metric value based on those predicted attributes. For
example, in at least some embodiments, the prediction component 230 may apply
a predetermined function to the predicted attributes for multiple features in
order to
compute a performance metric value. By way of example and not limitation, each
feature may have a weighting value associated with that feature. The
performance
metric value may, in at least some embodiments, be calculated as the sum of
the
products of the predicted attribute of features and the weighting value
associated
with that feature.
[00103] Next, at step 560, the monitoring component 232 may update the
monitoring schedule 202 based on the performance metric values determined at
step 550. For example, the scheduling component 232 may, at step 560, schedule
the monitoring of the locations 182, 184 in the location set 180 based on the
performance metric values determined at step 550. In at least some
embodiments, locations which have higher performance metric values may be
placed higher on the monitoring schedule 202 (and thus monitored sooner) than
locations with relatively lower performance metric values.
[00104] Thus, in the embodiment of FIG. 5, the monitoring schedule 202
is
determined in accordance with a plurality of predicted attributes. For
example, in
some embodiments, the monitoring schedule is determined in accordance with a
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first predicted attribute associated with a first feature and a second
predicted
attribute associated with a second feature.
[00105] Referring now to FIG. 6, a further process 600 for monitoring
content
stored at a plurality of locations 182, 184 (FIG. 1) in a location set 180
(FIG. 1) is
illustrated in flowchart form. The process 600 includes steps or operations
which
may be performed by the content monitoring system 160 of FIGs. 1 to 3. In at
least some embodiments, the content monitoring module 280 may be configured to
perform the steps or operations of the process 600 of FIG. 6. The steps or
operations of the process 600 of FIG. 6 may be performed by one or more of the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 of FIG. 2. That is, the content monitoring module 280, the
prediction component 230, the scheduling component 232 and/or the monitoring
component 234 may contain instructions for causing the processor 240 to
execute
the process 600 of FIG. 6.
[00106] The process 600 of FIG. 6 is similar to the process 500 of FIG. 5
except in that it includes a further step 660 of increasing the ranking of
stale
locations in the monitoring schedule 202. At step 660, the scheduling
component
232 may be increase the rank of a location in the monitoring schedule 202 if
that
location becomes stale. For example, the rank of a location may be increased
based on the period of time which has elapsed since the location was last
monitored. The period of time may be measured, for example, in terms of the
number of fetching or monitoring operations which have occurred by the
monitoring
component 234 since the location was last monitored. In some embodiments, the
rank of a location in the monitoring schedule 202 may be increased by
increasing
the performance metric value associated with that location. For example, the
predicted performance metric could be incremented by a predetermined amount
for
every thousand fetching operations. It will be appreciated however, that a
thousand fetching operations is intended to be illustrative and that other
thresholds
may be used.
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[00107] It will be appreciated that variations of the methods and
systems
described above are also possible. For example, various embodiments may omit
or
modify some of the steps of FIGs. 4 to 6.
[00108] While the present disclosure is primarily described in terms
of
methods, a person of ordinary skill in the art will understand that the
present
disclosure is also directed to various apparatus, such as a server and/or a
document
processing system, including components for performing at least some of the
aspects and features of the described methods, be it by way of hardware
components, software or any combination of the two, or in any other manner.
Moreover, an article of manufacture for use with the apparatus, such as a pre-
recorded storage device or other similar computer readable medium including
program instructions recorded thereon, or a computer data signal carrying
computer readable program instructions may direct an apparatus to facilitate
the
practice of the described methods. It is understood that such apparatus, and
articles of manufacture also come within the scope of the present disclosure.
[00109] While the processes 400, 500, 600 of FIGs. 4 to 6 have been
described
as occurring in a particular order, it will be appreciated by persons skilled
in the art
that some of the steps may be performed in a different order provided that the
result of the changed order of any given step will not prevent or impair the
occurrence of subsequent steps. Furthermore, some of the steps described above
may be combined in other embodiments, and some of the steps described above
may be separated into a number of sub-steps in other embodiments.
[00110] The various embodiments presented above are merely examples.
Variations of the embodiments described herein will be apparent to persons of
ordinary skill in the art, such variations being within the intended scope of
the
present disclosure. In particular, features from one or more of the above-
described
embodiments may be selected to create alternative embodiments comprised of a
sub-combination of features which may not be explicitly described above. In
addition, features from one or more of the above-described embodiments may be
selected and combined to create alternative embodiments comprised of a
combination of features which may not be explicitly described above. Features
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suitable for such combinations and sub-combinations would be readily apparent
to
persons skilled in the art upon review of the present disclosure as a whole.
The
subject matter described herein intends to cover and embrace all suitable
changes
in technology.
27
