U.S. patent application number 13/526424 was filed with the patent office on 2012-10-11 for knowledge-based data mining system.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Matthew Denesuk, Daniel Frederick Gruhl, Sridhar Rajagopalan, Andrew S. Tomkins.
Application Number | 20120259890 13/526424 |
Document ID | / |
Family ID | 29399635 |
Filed Date | 2012-10-11 |
United States Patent
Application |
20120259890 |
Kind Code |
A1 |
Denesuk; Matthew ; et
al. |
October 11, 2012 |
KNOWLEDGE-BASED DATA MINING SYSTEM
Abstract
In a data mining system, data is gathered into a data store
using, e.g., a Web crawler. The data is classified into entities.
Data miners use rules to process the entities and append respective
keys to the entities representing characteristics of the entities
as derived from rules embodied in the miners. With these keys,
characteristics of entities as defined by disparate expert authors
of the data miners are identified for use in responding to complex
data requests from customers.
Inventors: |
Denesuk; Matthew; (San Jose,
CA) ; Gruhl; Daniel Frederick; (San Jose, CA)
; Rajagopalan; Sridhar; (Saratoga, CA) ; Tomkins;
Andrew S.; (San Jose, CA) |
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
ARMONK
NY
|
Family ID: |
29399635 |
Appl. No.: |
13/526424 |
Filed: |
June 18, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10141327 |
May 8, 2002 |
8214391 |
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13526424 |
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Current U.S.
Class: |
707/776 ;
707/E17.014 |
Current CPC
Class: |
G06F 2216/03 20130101;
G06F 16/951 20190101 |
Class at
Publication: |
707/776 ;
707/E17.014 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1.-30. (canceled)
31. A computer-implemented method for collecting and associating
unstructured data, comprising: receiving a plurality of documents,
the documents comprising entities of interest; analyzing the
plurality of documents to identify the entities of interest,
wherein a direct linkage does not exist between at least a first
and a second identified entity of interest; and determining from
the plurality of documents a bridging entity, wherein: a first
direct linkage exists between the first identified entity of
interest and the bridging entity; and a second direct linkage
exists between the second identified entity of interest and the
bridging entity.
32. The computer-implemented method of claim 31, further comprising
determining a third direct linkage between a third identified
entity of interest and the bridging entity.
33. The computer-implemented method of claim 31, further comprising
finding documents other than the plurality of documents comprising
the third identified entity of interest.
34. The computer-implemented method of claim 31, further comprising
creating a dossier for the first identified entity of interest,
wherein the dossier comprises direct linkages and corresponding
entities for the first identified entity of interest.
35. The computer-implemented method of claim 34, further
comprising: periodically determining if another bridging entity
exists for the first identified entity of interest; and updating
the dossier if determined another bridging entity exists.
36. The computer-implemented method of claim 31, wherein entities
comprise at least one of the group consisting of: people, dates,
companies, institutions, publications, executed instruments,
events, and locations.
37. The computer-implemented method of claim 31, further comprising
periodically determining if another bridging entity exists.
38. A system for automatically collecting and associating
unstructured data, comprising: a storage configured to receive a
plurality of documents, the documents comprising entities of
interest; a data miner configured to: analyze the plurality of
documents to identify the entities of interest, wherein a direct
linkage does not exist between at least a first and a second
identified entity of interest; and determine from the plurality of
documents a bridging entity, wherein: a first direct linkage exists
between the first identified entity of interest and the bridging
entity; and a second direct linkage exists between the second
identified entity of interest and the bridging entity.
39. The system of claim 38, wherein the data miner is further
configured to determine a third direct linkage between a third
identified entity of interest and the bridging entity.
40. The system of claim 38, further comprising an aggregator
configured to: find documents other than the plurality of documents
comprising the third identified entity of interest; and send the
found documents to the storage.
41. The system of claim 38, further comprising a creation module to
create a dossier for the first identified entity of interest,
wherein the dossier comprises direct linkages and corresponding
entities for the first identified entity of interest.
42. The system of claim 41, wherein: the data miner is further
configured to periodically determine if another bridging entity
exists for the first identified entity of interest; and the
creation module is further configured to update the dossier if
determined another bridging entity exists.
43. The system of claim 38, wherein entities comprise at least one
of the group consisting of: people, dates, companies, institutions,
publications, executed instruments, events, and locations.
44. The system of claim 38, wherein the data miner is further
configured to periodically determine if another bridging entity
exists.
45. A computer-implemented method for analyzing buzz regarding a
product or service, comprising: receiving a plurality of documents;
in the plurality of documents, identifying locations where a
product or service of interest is mentioned based on a determined
context of the mention of the product or service of interest; and
determining from the mentions a buzz associated with the product or
service of interest.
46. The computer-implemented method of claim 45, further
comprising: determining a buzz associated with a competing product
or service; and comparing the buzz associated with the product or
service of interest to the buzz associated with the competing
product or service of interest.
47. The computer-implemented method of claim 45, further
comprising: determining the buzz associated with the product or
service of interest periodically over time; and tracking trends
over time in buzz associated with the product or service of
interest.
48. The computer-implemented method of claim 47, further
comprising: identifying any trends to be alerted to a user; and for
any trends to be alerted, alerting the user of the trends.
49. The computer-implemented method of claim 47, further comprising
determining an effect of marketing activities for the product or
service of interest on the trends.
50. The computer-implemented method of claim 49, further
comprising: segmenting the buzz based on at least one of geography
or demographics; segmenting the marketing activities based on at
least one of geography or demographics; and determining a
correlation between the segmented marketing activities and the
segmented buzz.
51. The computer-implemented method of claim 50, wherein marketing
activities comprises advertisement spending.
52. The computer-implemented method of claim 49, wherein marketing
activities comprises advertisement spending.
53. A system for analyzing buzz regarding a product or service,
comprising: a receiver to receive a plurality of documents; a data
miner to: in the plurality of documents locations, identify where a
product or service of interest is mentioned based on a determined
context of the mention of the product or service of interest;
determine a buzz associated with the product or service of interest
periodically over time; and determine trend over time in buzz
associated with the product or service of interest.
54. The system of claim 53, the data miner further to determine an
effect of marketing activities for the product or service of
interest on the trend.
55. The system of claim 53, the data miner further to: segment the
buzz based on at least one of geography or demographics; receive
information regarding marketing activities for the product or
service of interest; segment the marketing activity information
based on at least one of geography or demographics; and determine a
correlation between the segmented marketing activity information
and the segmented buzz.
Description
FIELD OF THE INVENTION
[0001] The present invention relates generally to data mining
systems.
BACKGROUND OF THE INVENTION
[0002] Data mining is the process of extracting user-desired
information from a corpus of information. Perhaps the most
widespread example of data mining is the search engine capability
incorporated into most Web browsers, which allows users to enter
key words and which then return a list of documents (sometimes
listing several thousands of documents) that the user then sifts
through to find the information he or she desires.
[0003] Existing search engines such as AltaVista, Google, Northern
Light, FAST, and Inktomi work by "crawling" the Web, i.e., they
access Web pages and pages to which the accessed pages hyperlink,
generating an inverted index of words that occur on the Web pages.
The index correlates words with the identifications (referred to as
"uniform resource locators", or "URLs") of pages that have the key
words in them. Queries are responded to by accessing the index
using the requested key words as entering arguments, and then
returning from the index the URLs that satisfy the queries. The
page identifications that are returned are usually ranked by
relevance using, e.g., link information or key word frequency of
occurrence.
[0004] Despite the relevancy ranking used by most commercial search
engines, finding particular types of information typically entails
a great deal of mundane sifting through query results by a person.
This is because expertise in a particular area often is required to
separate the wheat from the chaff. Indeed, as recognized by the
present invention, it may be the case that one expert is required
to process documents using his or her expert criteria to winnow out
a subset of the documents, and a second expert must then use his or
her expert criteria to locate the required information in the
subset from the first expert. This is labor-intensive and mundane
and, despite being merely a necessary precursor to the higher level
work of using the data, can consume more time than any other phase
of a project.
[0005] Consider, for example, responding to a complex marketing
question, such as, "what do our commercial customers in the Pacific
Northwest think of our competitor's health care products in terms
of brand name strength and value?" An analysis of Web pages might
begin with a key word search using the name of the competitor, but
then considerable expert time would be required to eliminate
perhaps many thousands of otherwise relevant documents, such as
government reports, that might be useless in responding to the
question. Many more documents might remain after the first
filtering step that are even more afield, such as teenager chat
room documents, that might mention the competitor's name but that
would require expertise in what types of demographics constitute
the targeted segment to eliminate.
[0006] Or consider the simple question, "Is Adobe Acrobat7
compatible with MS Word7?" This simple query, posed to one of the
above-mentioned search engines, yielded a results set of 33 million
Web pages, most of which would not have contained the "yes" or "no"
answer that is sought. Eliminating the useless pages would require
an expert to look at each page and determine whether it was the
type of page that might contain information on program
compatibility. Another expert might then be required to examine the
pages passed on from the first expert to determine if, in fact, the
pages contained the answer to the specific question that was posed.
It will readily be appreciated that cascading expert rules to sift
through a large body of information can consume an excessive amount
of time.
SUMMARY OF THE INVENTION
[0007] A system includes a data store, and at least one lower level
analysis engine communicating with the data store and generating an
output using a first set of rules. At least one higher level
analysis engine receives the output of the lower level analysis
engine and generates an output using a second set of rules.
[0008] In a preferred embodiment the engines, which can be referred
to as "data miners", associate respective keys with entities in the
data store. The keys represent respective characteristics of the
entity. The higher level miner can receive data from the data store
only if the corresponding entity is associated with a key output by
the lower level miner.
[0009] A large number of data miners can be employed. By way of
non-limiting example only, the miners can include a pornography
filter, a spam filter, a link miner to identify links associated
with Web pages in the data store, a classification miner
classifying documents based on the occurrence of patterns of terms
in the document, a geospatial miner identifying geographic
information on a document page, a corporations miner, a taxonomies
miner returning documents having a predefined taxonomy category, a
regular expression (regex) miner providing a stream of pages
containing a defined regex, and a personnel miner.
[0010] In another aspect, a data mining system includes a data
store holding data classified into entities. Plural data miners use
rules, including statistically-based rules and expert rules, to
process the entities and append respective keys to the entities
representing characteristics of the entities as derived from rules
embodied in the miners. Thereby, characteristics of entities as
defined by expert authors of the data miners are identified for use
in responding to data requests from customers.
[0011] In yet another aspect, a method for extracting data from a
data store includes accessing entities in the data store, and
processing the entities using a first set of rules to identify a
first characteristic of the entities. For entities having the first
characteristic, a representation of the characteristic, such as a
key, is associated with the entities. The method then includes
receiving as input to a second set of rules only entities that have
the first characteristic, based on the representation of the
characteristic. The entities that have the first characteristic are
then processed using the second set of rules to identify at least a
second characteristic of the entities.
[0012] A computer program device is also disclosed that can be read
by a processing system for data mining. The device includes means
for undertaking the inventive method disclosed herein.
[0013] The details of the present invention, both as to its
structure and operation, can best be understood in reference to the
accompanying drawings, in which like reference numerals refer to
like parts, and in which:
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a block diagram of a preferred system
architecture;
[0015] FIG. 2 is a flow chart of the overall logic;
[0016] FIG. 3 is a schematic diagram of a horizontal table;
[0017] FIG. 4 is a schematic diagram of a vertical table; and
[0018] FIG. 5 is a flow chart of an exemplary miner logic.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0019] Referring initially to FIG. 1, a system is shown, generally
designated 10, for responding to customer requests for data. The
system 10 essentially integrates the knowledge of many experts to
sift through a large corpus of data to respond to what might be
relatively complex requests for information, such as those
discussed above. In non-limiting embodiments, the system 10 can be
used for enterprise data analysis, competitive intelligence,
trending, discovery, web portal services, clustering and taxonomy
creation. Also, the system 10 can be used to support targeted
functions which require significant embedded expertise such as a
suite of procurement-specific services (of interest to a particular
part of the enterprise).
[0020] The system 10 can be hosted at a single vendor location on
one or a cluster of processors to respond to customer requests for
data in a service format. Or, portions of the system 10 can be
provided to customers for execution of data mining at customer
facilities.
[0021] As set forth in further detail below, the system 10 includes
a data gathering layer, a data storage layer, a data mining layer,
a data presentation layer, and a system management layer.
Commencing at the left in FIG. 1 and first addressing the data
gathering layer, a Web crawler 12 accesses the World Wide Web 14
(and if desired other portions of the Internet). Intranets 16, for
example, may also be accessed by the crawler 12, including
proprietary information available only through proper
authentication. Preferably, the crawler 12 continuously crawls the
Web 14, with some pages being crawled more often than others based
on frequency of page updates and other criteria, and outputs the
crawled pages to a data store 18 using a data layer application
programming interface (API) 20. In one preferred, non-limiting
embodiment, the interface 20 is IBM's service-oriented protocol
known as "Vinci xTalk", which is a lightweight XML-based protocol
coupled with a set of usage conventions covering monitoring,
logging, and data transfer. Network-level APIs within the system
are specified in terms of xTAlk frames.
[0022] Also, the preferred crawler includes a feedback channel,
whereby its operation may be changed as desired. In one preferred,
non-limiting embodiment, the crawler 12 is that disclosed in U.S.
Pat. No. 6,263,364, incorporated herein by reference, or the
crawler set forth in IBM's co-pending U.S. patent application Ser.
No. 09/239,921 entitled "SYSTEM AND METHOD FOR FOCUSSED WEB
CRAWLING", also incorporated herein by reference. In addition to
obtaining data using the crawler 12, the system 10 can include, if
desired, a structured data gatherer 22 that processes data from
customer and third party databases 24 and sends the processed data
to the data store 18.
[0023] With respect to the data store 18, in one embodiment the
data store 18 is a relational database system (RDBMS) such as IBM's
DB2 system. In other embodiments, other systems, such as file
systems, can be used. The disclosure below applies to both types of
data stores.
[0024] In one embodiment, the store 18 can include a centralized
program executing on a single computer or on multiple computers.
The below-described miners can execute on independent computers,
making requests to the store program to read and write data.
Alternatively, the store 18 can be distributed across multiple
computers, with the miners executing in parallel on those
computers. In such an embodiment, a document can be read from the
local portion of the store into memory, passed through in-memory
through a chain of dependent or independent miners, and written
back into the store, to facilitate efficient use of resources.
Indeed, both architectures might exist in the same system 10,
recognizing that some miners operate better in the second
architecture (e.g., miners that operate on a per-page basis) while
other miners might require the additional overhead of the first
architecture.
[0025] The data store 18 is associated with an indexer 26 and, if
desired, a fast, semiconductor-implemented cache 28. A query
processor 30 can access the cache 28, indexer 26, and data store 18
to execute miner queries as set forth below. Miner work queues,
discussed below, can be implemented as part of the data storage
layer of the system 10.
[0026] The data store 18 contains a relatively large corpus of
data, e.g., Web page data from the crawler 12. Also, the data store
18 contains entities that represent the underlying data, as set
forth further below. These entities have respective universal
identities (UEIDs) that encode the entity identification and entity
type, e.g., "web page", "hyperlink", "person", "corporation",
"article". Also, entities can contain keys with associated key
values as appended to the entities by the below-described data
miners. The page entity, for example, is processed by a miner that
creates a key called "Crawl:Content" which contains the http
content of the associated webpage (the key value is thus relatively
long). In any case, the entities can be stored in a file system, a
database system such as DB2 in which they are represented in both
horizontal and vertical tables, or other storage system.
[0027] The indexer 26 includes, among other things, indices of keys
and key values found in the store. The indexer 26 can contain
Boolean indices, which store "yes" or "no" values to queries of the
form, "does key k have value v?" Also, the indexer 26 can contain
range indices, which store ranges of key values, e.g., geographic
ranges, text indices, which are conventional indices for the
underlying date, and other indices as desired.
[0028] In any case, the preferred indices (and, when the data store
is a database, the data storage tables) do not indicate where on,
e.g., a Web page a particular name or text might occur, but only
that a page has a particular characteristic, or that a particular
textual element appears somewhere on the page. In this way, the
relatively high granularity of the system 10 data store requires
less storage space than would otherwise be required, facilitating
practical implementation. If desired, however, it may be indicated
where on, e.g., a Web page a particular name or text might
occur.
[0029] With respect to additional details of the data storage layer
of the system 10, the preferred, non-limiting indexer 26 is a
generalization of a conventional inverted file text indexer. In one
instantiation, it indexes web documents and provides a keyword
search application programming interface (API) to the documents.
The set of keywords associated with a document could be simply the
words of the document, or it could be augmented by the miners
discussed below with additional information as necessary, such as
geographic locations on the page, proper names, references to
products or restaurants or other entities known to the system 10,
results of semantic analysis of the page, and so forth. The keyword
search API then allows queries to include any of these extended
sets of keywords.
[0030] In other instantiations, the indexer 26 holds defined keys
for a particular entity to allow boolean queries, or graph data to
support inlink and outlink queries, and so forth. To provide this
generality, tokenization is decoupled from indexing. More
particularly, the indexer 26 expects to receive a stream of tokens
rather than a stream of documents. Accordingly, tokenizing is
undertaken prior to indexing. With each indexed token, the token
location (the token offset position in the stream) is stored along
with user-defined token data, which can be arbitrary. This
simplified model facilitates efficient indexing and provides a
general purpose API for use in a variety of applications. Moreover,
decoupling allows tokens from different embodiments of rules (e.g.,
from different miners) to be indexed together.
[0031] Several versions of the indexer 26 may simultaneously
execute. For simplicity, a "primary" text indexer is considered
that holds tokens corresponding to the entire set of crawled pages.
As discussed below in relation to the data miners of the present
invention, miners attach "keys" to entities that are stored in the
data store 18. Tokenizers associated with the indexer 26 follow
exactly this approach. In one non-limiting embodiment, the textual
tokenizer can be based on the TAF (Text Analysis Framework)
tokenizer produced by IBM Research and IBM Software in Boeblingen.
This tokenizer reads page data, and writes for each page the result
of base tokenization. Other tokenizers can then consume that data,
or consume the raw page data as they choose, and write other tokens
to the store. For instance, a tokenizer might match proper names
and tag them as such, and another might read only the output of the
proper name tokenizer, and might write tokens containing metadata
mapping proper names to particular known entities elsewhere in the
system 10. All of these tokenizers register with the primary
indexer 26.
[0032] Having set forth details of the preferred, non-limiting
indexer 26, attention is directed to the query processor 30.
Streams of data from the data store 18 may be requested by the
below-described miners using an extensible query language to invoke
the query processor 30. The paradigm for accessing the query
processor 30 is exactly the same as the paradigm for accessing the
indexer 26, i.e., the requester sends a service-specific query (in
this case, a statement in an extensible query language), and
receives back from the query processor 30 a data stream. The query
might involve combining several streams using standard stream
combinators (boolean operators such as AND and OR, database join
operators such as inner and outer joins, sort operators, and
operators that augment a stream with additional information by,
e.g., augmenting each UEID in the stream with the value of a
certain key). The query language can join together arbitrary
streams.
[0033] As mentioned above, the data mining layer of the system 10
includes a miner library 32 that contains software-implemented data
miners which communicate with the data layer API 20 and, hence,
with the data storage layer. In the exemplary, non-limiting
embodiment shown, the miner library 32 includes a link miner 34
which returns links to/from a page, a spam filter 36 for
identifying "spam" in the data store 18, a porn filter 38 for
identifying pornographic pages in the data store 18, a
classification miner 42 that classifies pages based on the
occurrence of patterns of terms in the pages, a geospatial miner 44
which identifies any geographic information on a Web page, a
corporations miner 46, a taxonomies miner 48 that returns pages
having a predefined taxonomy category, a regular expression (regex)
miner 50 that provides a stream of pages containing a defined
regex, and so on.
[0034] By "miner" or "data mining element" is meant an analysis
engine that generates an output, and specifically an output that
can include one or more keys representing characteristics of an
entity, using a set of rules. These rules can be heuristically
determined, and can include statistically-based rules. By way of
non-limiting example, the "porn filter" miner 38 might determine
whether a Web page contains pornography using image analysis
techniques, and append a key and Boolean key value to a page that
indicates "porn=yes" or "porn=no". As one non-limiting example, the
porn miner can use the principles set forth in IBM's U.S. Pat. No.
6,295,559. Or, the corporations miner 46 might determine whether a
particular page is a corporate page using word association rules,
URL analysis, or other method, and then append a key to the page
that indicates the result of the miner's analysis. Yet again, the
miner that establishes the spam filter 36 can use, e.g., the
principles set forth in IBM's U.S. Pat. No. 6,266,692 to append
keys to Web pages or emails indicating whether they are "spam".
Still further, the geospatial miner 44 might append a key to a Web
page representing a latitude and longitude range associated with
the subject or author of the page, based on rules for deriving such
information. As one non-limiting example, the geospatial miner can
use the principles set forth in IBM's U.S. Pat. No. 6,285,996. All
of the above-referenced patents are incorporated herein by
reference. It is to be appreciated that the particular types of
miners and the particular rules employed by each miner may vary
without affecting the scope or operation of the present
invention.
[0035] In any case, the data miners are modular components that
have specific input and output specifications. They may be written
in any language, and may range from, e.g., a few lines of simple
perl to spot keywords, to tens of thousands of lines of code (or
more) to perform complex distributed operations. Large problems may
be broken into smaller pieces, each of which may be easily tackled
by a single miner or miner writer. The resulting intermediate
results can be easily viewed, checked and debugged, and may also be
of independent interest to other miner writers. In this way, miners
represent the service-oriented architecture equivalent of
object-oriented design. Miners are specified in terms of the data,
usually as indicated by the below-described keys, that must be
available when they start, and the data (including other keys) that
they will create during successful processing.
[0036] Specifically, in a preferred embodiment a miner can consume
work from a system-managed work queue, based on one or more
dependencies that are specified by the miner. As an example, a
miner ("Miner A") that is interested in processing pages that
contain references to certain personalities or certain geographic
locations might register a dependency on the geospatial miner 44
and a person miner. The work queue for Miner A will then be
continuously updated to contain entities that have been processed
by the geospatial and person miners as indicated by keys appended
to the entities in the data store by the geospatial and person
miners, but not yet by Miner A. After processing those entities,
Miner A could append its own key or keys to the processed entities
using, when the data store is a database, existing entity tables,
or it could create new entities (with corresponding tables when the
data store is implemented as a database), with each key
representing a characteristic of the entity. Miners that extract
references to particular products, brand names, people, industry
segments, artists, and so forth operate in this mode.
[0037] On the other hand, a miner might not consume work from a
queue, but rather might register freshness requirements with the
below-described management system controlling how often and in what
environment the miner must be run. Other miners that, for instance,
run a weekly aggregate computation might ask the below-discussed
management system to initiate one or more instances of the miner in
order to complete a weekly build of the resulting aggregate table
or data structure, again using as input entities that have the
appropriate keys appended to them.
[0038] Miners thus read long-running persistent and reliable
streams of raw content, as well as processed data created by other
miners, from the data store 18. These miners, and in fact many
miners within the system 10, will consume and process data. The two
models for data access discussed above include random access to a
particular entity or set of entities, and stream access to an
enumeration of entities. To perform a random access on the data
store 18, a miner simply requests relevant pieces of the entity in
question using the UEID. To receive a data stream, an enumeration
is initiated by requesting data from the data store 18 using the
indexer 26 or using the query processor 30. For instance, miners
with more sophisticated data requirements may specify to the query
processor 30 complex queries that may require access to multiple
components, with query optimization being conventionally undertaken
and streams of data generated in return. Such queries could entail
database joins across multiple tables, index lookups including text
search, range queries, geographic lookups, and composition of
smaller result sets from many different sources within the system.
Whether derived from the indexer 26 or query processor 30,
enumerations provide persistence, and can be accessed either in
serial or in parallel depending on the nature of the
processing.
[0039] Miners write back the results of their processing to the
data store 18 for other miners and end users to access. As
discussed above, to write data back into the store 18 for other
miners to access, a miner simply creates the new keys and values it
wishes to attach to the entity, then perform a store write
operation.
[0040] The results of a particular customer request for information
as provided by the miners of the present invention may be presented
on a data presentation layer 52. The results may be printed, or
presented in audio-video form, or other form as desired. A cluster
management subsystem layer 54 manages the above-discussed layers as
more fully set forth below. If desired, a customer interface 56 can
access the data layer API 20 and customer databases 58, to
facilitate entering and responding to customer requests for
information.
[0041] In accordance with the presently preferred embodiment, the
management subsystem layer 54 schedules, initiates, monitors, and
logs operations within the various components. End applications
draw results from rendered tables, from the data store 18, or from
real-time query-processing miners.
[0042] In a preferred, non-limiting embodiment, a large cluster of
computers hosts the system 10 and management subsystem layer 54. In
addition to managing the miners, the managements system 54 detects
hardware and software failures in the cluster and programmatically
recovers from the failures, notifying system managers as
appropriate. The management subsystem layer 54 also provides
functionality such as relocation, load balancing and scheduling for
each software component.
[0043] All system 10 events are gathered into a single information
server, which maintains status, statistics, logging, and error
codes from applications and infrastructure components. Events are
generated from a wide range of sources including error classes used
by software components in the cluster, a DB2 event and log monitor
associated with the data store 18, system and network monitoring
components, and so-called "Nanny" agents that are part of the
management subsystem layer 54 and that execute on respective
computers of the cluster.
[0044] The preferred "Nanny" agents start, stop, and monitor
processes, and track computer resources, on their respective
computers. They undertake and/or monitor "pings", disk utilization,
memory utilization, processor utilization, kernel resource
utilization (processes, sockets, etc), and process controls
including start, stop, killall. "Nanny" agents also receive status
from individual miners running on their respective computers,
including log messages, error reports, statistics, number of
waiting documents, number of processed documents per second, net
document flow rate, processing rate in bytes or entities per
second, and other miner-specific status reports.
[0045] FIG. 2 sets forth the overall logic of system 10 operation
discussed above. Commencing at block 60, the crawler 12 crawls the
Web 14 to add data to the data store 18. If desired, the data store
18 can be augmented with data at block 62 from the databases 24 by
means of the data gatherer 22.
[0046] Once the data store 18 contains data, the logic may flow to
block 64, wherein at least some of the miners, which might be
thought of as "low level" miners, access data and process it in
accordance with the disclosure above. The low level miners write
the results back to the data store 18. For example, filtering
miners such as the SPAM filter 36 and porn filter 38 might process
all Web pages in the data store 18 and write back respective keys
to the corresponding entities indicating whether each site is SPAM
or pornography. Moreover, a detag miner can be invoked on each page
to process the page contents by removing hypertext markup language
(html) mark-ups, leaving only the raw text, and then append a
"detag" key so indicating to each corresponding entity.
[0047] Moving to block 66, customer requests for information can be
received. At block 68, additional low level miners can be written
in response, or high level miners, if required and not yet written,
can be created. High level miners can be thought of as miners that
specify dependencies on the outputs of other miners, i.e., that
require entities for processing that have been tagged with keys
output by lower level miners.
[0048] An example of a higher level miner might be one that
responds to the query, "what do our commercial customers in the
Pacific Northwest think of our competitor's health care products in
terms of brand name strength and value?" Such a miner might specify
that it wishes to receive only pages from the Pacific Northwest, as
indicated by a geospatial key appended to entities by the
geospatial miner, and only if the competitor's name is featured in
the entity, as indicated by a key appended to the entity by a
proper name miner. Many such dependencies might be hypothesized, it
being understood that the expert who might specify the dependencies
of such a miner uses heuristics according to his or her expertise
without having to know how the expert who wrote, e.g., the
geospatial miner arrived at his or her solution. The results are
provided to the customer at block 70 and the customer is billed, on
a per request basis or on a subscription basis.
[0049] FIGS. 3 and 4 illustrate the structure of the horizontal and
vertical tables that can be used when, by way of non-limiting
example, the data store is implemented by a database system such as
DB2. A horizontal table 72 is shown in FIG. 3 wherein each row 74
represents an entity. Each row has a UEID column 76, if desired a
timestamp column 78, and plural key columns 80. In contrast, a
vertical table 82 shown in FIG. 4 includes plural rows 84, each
including a single key column 86, UEID column 88, key code column
90 indicating the type of key, and a key value column 92 indicating
the value of the key, e.g., Boolean value, range value, etc. A
timestamp column 94 can be included if desired, indicating the time
the associated entry was made in the table.
[0050] From the above discussion it is to be appreciated that the
data store 18, by means of the tables 72, 82 in the database
implementation, abstracts the layout of the actual data, so that
the decision on which type of tables to use for a specific entity
can be made to benefit performance for the access patterns that are
expected to be typical for that entity. The preferred data store 18
also abstracts DB2's limits on row length by automatically using
either VARCHARs or BLOBs to store values that are longer than the
maximum row length. APIs are provided to help programmers access
the DB2 database directly to write code that is independent of the
physical layout of the data.
[0051] For example, the crawler 12 writes the Crawl:Content key,
the Crawl:Header key, and a number of extracted metadata keys such
as the URL, the fetch latency, the last date on which the page
changed, the server, the HTTP return code, and so on. Within the
data store 18 when implemented as a database, this information is
all written into a single horizontal table with one column for each
crawler key. It is written only by the crawler 12, but it may be
read by any miner having permission. Miners requiring the content
of a page need only ask for the value of the Crawl:Content key, and
the data store 18 maps to the appropriate table.
[0052] To facilitate this computation, the data store 18 can if
desired provide a data dictionary whose purpose is to provide
information on the mapping of a key to an actual location within
the relational database. In addition, it provides ancillary
information such as the type and owner of the key. Miners that
write multiple keys may write those keys into a specific horizontal
table so that many keys can be written in a single row update
operation.
[0053] As mentioned above, a number of miners, including the
crawler 12, operate most naturally at the page level to create and
consume per-page information. However, other miners can also
operate on entities other than raw pages. For instance, some miners
such as a link-based spam filter 36 operate on entire web sites to
decide whether an entire site is spam or not. Other miners might
operate on phrases, or on proper names, or company names, or
places, restaurants, employers, and so forth. Each such category
represents a separate entity, and requires its own set of
horizontal and vertical tables (or other data storage structure)
within the data store 18. Accordingly, in the same way that the
crawler 12 writes to a horizontal table within the page entity in
the database implementation, the corporation miner 46 might
populate a horizontal table for corporations. Other miners that
wish to attach key-value pairs to corporations might access keys
appended to entities by the corporation miner 46, and then write
other keys into other data structure of the corporation entity.
[0054] FIG. 5 shows a specific logic flow that might be followed
when a Web page arrives from the crawler 12 at block 96. At block
98 a detag miner can be invoked to process the page at block 100 by
stripping html markups, leaving only the raw text, and appending a
"detag" key so indicating to the entity.
[0055] Proceeding to block 102, other miners can receive the entity
in accordance with principles set forth above by having the system
manager 54 deliver the entity to such other miners based on the
detag key. At block 104 the other miners process the data
underlying the entity and can append their own keys to the entity's
data structure entry, in, e.g., both the horizontal table
representing the entity and the associated vertical table
representing the key when the data store is implemented as a
database. Also, some miners might extract information, e.g., a
corporate name from, e.g., a page entity and create additional
entity data storage structures (such as files or tables)
representing such entities, e.g., corporation entities.
[0056] After initial miner processing, the logic can move to
decision diamond 106, wherein it is determined whether still
further miners, e.g., an n.sup.th miner, has requested entities
having predetermined keys. If all keys required as input by the
n.sup.th miner are present in an entity, the entity is provided to
the n.sup.th miner at block 108 by, e.g., placing the entity in the
miner's work queue. The n.sup.th miner then accesses the entity at
block 110 by, e.g., accessing its work queue to process the entity
and/or processing the underlying data of the entity. At block 112,
the n.sup.th miner outputs its own key or keys and enters these
keys in the entity data structures as appropriate to associate the
key or keys with the entity. Then, at block 114 a customer's miner
can invoke other miners and/or access entities as appropriate to
create a database containing information sought by the
customer.
[0057] The system 10 described herein can be used for many specific
customer applications. One such application is an "action
link"/"drill note" application in which a document is fed into the
system, and system miners identify important "entities" in the
document (e.g., people, places, events) based on rules. A
compilation miner in the system 10 then compiles a dossier or other
form of information collection on each of these entities. The
dossier (or equivalent) is then linked to the entity in the
original document.
[0058] The dossier or equivalent may be a mini-portal for that
entity, e.g., it may look like a Yahoo 7-type directory
specifically for that entity. Accordingly, if the entity is a
person, one may have subcategories for that person consisting of
addresses associated with that person, people associated with that
person, locations associated with that person, industries
associated with that person, publication about that person, etc.
The entities that are selected to be "action-linked" are determined
by the compilation miner, preferably in accord with a tunable
propensity function or other rule that can be heuristically
determined.
[0059] As another non-limiting example of how the system 10 can be
used, a "Legal Leads Finder and Builder" application can include
the above-mentioned "action links"/"drill notes" as one component,
and also look for probability linkages between "entities," wherein
the entities are contained in the data store 18, but at least some
of the linkage elements sought may not be. As an example, consider
a litigation case in which John Doe and Jane Smith (people
entities) are both listed in a people entity data structure, but
the data in the data store 18 contains no manifest relationship
between them. A Legal Leads Finder and Builder miner identifies the
two entities as important entities based on a set of rules, and
then determines whether other entities exist through which the two
people entities might be related. For example, it might happen that
both people are on the Board of a particular company or charity;
they may have published a paper together; they may have been
mentioned in the press as colleagues or partners in some kind of
deal; and so on. In these cases, the linking entity (e.g., the
company for which both John and Jane are Board members; the paper
which they published together) may be regarded a "bridging entity"
and be included in discovery requests.
[0060] Consequently, the discovery phase of litigation can be
expanded to request not only documents pertaining directly to
certain topics, people, or events, but also to locate documents
that are relevant to the "bridging entities" through external data
sources.
[0061] As another non-limiting example of how the system 10 can be
used, a competitive product marketing application can be
implemented. Information about a set of products can be fed into
the system 10, and miners can be constructed to unambiguously
identify and classify mentions of the product based on the context
of the mention. For example, Tide 7 as a detergent product can be
distinguished from the natural phenomena of tides. Also, a
classification/profiler miner, preferably using statistical means
to classify/profile a mention based on a set of previously
classified/profiled mentions, is used to classify the context of
the mentions. Further, a geography miner can be used to determine
appropriate geographic linkages associated with the source in which
the mention occurs. The application can then be made to provide a
finely divided measure of the "ink" or "buzz" that some set of
their products are receiving and compare this "ink" or "buzz" with
that corresponding to competitive products. This information can be
presented on a map, for example, with different colors or
brightness levels representing the magnitude of "ink" or "buzz."
This information can also be tracked over time, assisting in the
identification of positive or negative trends that deserve
attention. As another feature, geographically and demographically
segmented data representing ad spending or other marketing
activities associated with a product can be fed into a system, and
a miner can test for forward correlations of such activities to
"ink" or "buzz," thereby providing some measure of the
effectiveness of the marketing activities.
[0062] While the particular KNOWLEDGE-BASED DATA MINING SYSTEM as
herein shown and described in detail is fully capable of attaining
the above-described objects of the invention, it is to be
understood that it is the presently preferred embodiment of the
present invention and is thus representative of the subject matter
which is broadly contemplated by the present invention, that the
scope of the present invention fully encompasses other embodiments
which may become obvious to those skilled in the art, and that the
scope of the present invention is accordingly to be limited by
nothing other than the appended claims, in which reference to an
element in the singular is not intended to mean "one and only one"
unless explicitly so stated, but rather "one or more". All
structural and functional equivalents to the elements of the
above-described preferred embodiment that are known or later come
to be known to those of ordinary skill in the art are expressly
incorporated herein by reference and are intended to be encompassed
by the present claims. Moreover, it is not necessary for a device
or method to address each and every problem sought to be solved by
the present invention, for it to be encompassed by the present
claims. Furthermore, no element, component, or method step in the
present disclosure is intended to be dedicated to the public
regardless of whether the element, component, or method step is
explicitly recited in the claims. No claim element herein is to be
construed under the provisions of 35 U.S.C. '112, sixth paragraph,
unless the element is expressly recited using the phrase "means
for" or, in the case of a method claim, the element is recited as a
"step" instead of an "act".
* * * * *