Data search system and method using mutual subsethood measures

• US 20040034633A1
• Filed: 03/14/2003
• Published: 02/19/2004
• Est. Priority Date: 08/05/2002
• Status: Abandoned Application

• Current Assignee: ORINCON Corp. International (Martin Marietta Corporation)
• Original Assignee: ORINCON Corp. International (Martin Marietta Corporation)
• Inventors: Rickard, John Terrell
• Application Number:
  US10/389,049
• Publication Number:
  US 20040034633A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/2468 Fuzzy queries
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A non-textual data searching system according to the invention is capable of searching non-textual data at semantic levels above the fundamental symbolic level. The general approach begins by indexing the non-textual data corpus in such a way as to facilitate searching. The indexing process results in a number of “keytroids” that represent clusters of fuzzy attribute vectors, where each fuzzy attribute vector represents a data event associated with one or more non-textual data points. The actual searching process is analogous to a conventional text-based search engine: a query vector, which identifies a number of fuzzy attributes of the desired data, is processed to retrieve and rank a number of keytroids. The keytroids can be inverse-mapped to obtain data events and/or non-textual data points that satisfy the query.

Claims

1. A data search method comprising:

receiving a query vector specifying a searching set of fuzzy attribute values for a collection of data;

calculating mutual subsethood measures between said query vector and a plurality of keytroids in a keytroid database, each keytroid in said keytroid database specifying a respective set of fuzzy attribute values for said collection of data; and

retrieving a subset of keytroids from said keytroid database, each keytroid in said subset of keytroids satisfying a threshold mutual subsethood measure.

2. A method according to claim 1, further comprising ranking said subset of keytroids based upon relevance to said query vector.

3. A method according to claim 2, wherein ranking said subset of keytroids is based upon said mutual subsethood measures.

4. A method according to claim 2, wherein:

each of said plurality of keytroids is associated with a plurality of data points in said collection of data; and

said method further comprises ranking, for each keytroid in said subset of keytroids, said data points associated therewith.

5. A method according to claim 1, wherein:

said query vector is a fuzzy subset of each of said plurality of keytroids; and

each of said plurality of keytroids is a fuzzy subset of said query vector.

6. A method according to claim 1, wherein calculating mutual subsethood measures incorporates dimensional importance weighting of said fuzzy attribute values.

7. A method according to claim 1, wherein said collection of data is a collection of non-textual data.

8. A method according to claim 7, wherein each of said plurality of keytroids indicates at least one non-textual data event associated with one or more non-textual data points from said collection of non-textual data points.

9. A method according to claim 1, wherein said calculating step compares said query vector to each keytroid in said keytroid database.

10. A method according to claim 1, wherein:

said query vector specifies up to n fuzzy attributes; and

each of said plurality of keytroids specifies n fuzzy attributes.

11. A data search system comprising:

a query input component configured to receive a query vector specifying a searching set of fuzzy attribute values for a collection of data;

a keytroid database containing keytroids, each specifying a respective set of fuzzy attribute values for said collection of data; and

a query processing component configured to calculate mutual subsethood measures between said query vector and a plurality of keytroids in said keytroid database, and to retrieve a subset of keytroids from said keytroid database, each keytroid in said subset of keytroids satisfying a threshold mutual subsethood measure.

12. A system according to claim 11, further comprising a ranking component configured to rank said subset of keytroids based upon relevance to said query vector.

13. A system according to claim 12, wherein said ranking component ranks said subset of keytroids based upon said mutual subsethood measures.

14. A system according to claim 11, further comprising a data retrieval component configured to retrieve at least one data point corresponding to at least one keytroid in said subset of keytroids.

15. A system according to claim 11, wherein:

said query vector is a fuzzy subset of each of said plurality of keytroids; and

each of said plurality of keytroids is a fuzzy subset of said query vector.

16. A system according to claim 11, wherein said query processing component calculates said mutual subsethood measures by applying dimensional importance weighting of said fuzzy attribute values.

17. A system according to claim 11, wherein said collection of data is a collection of non-textual data.

18. A system according to claim 17, wherein each of said plurality of keytroids indicates at least one non-textual data event associated with one or more non-textual data points.

19. A system according to claim 11, wherein said query processing component calculates mutual subsethood measures between said query vector and each keytroid in said keytroid database.

20. A system according to claim 11, wherein:

said query vector specifies at least n fuzzy attributes; and

each of said plurality of keytroids specifies n fuzzy attributes.

21. A computer program for searching non-textual data, said computer program being embodied on a computer-readable medium, said computer program having computer-executable instructions for carrying out a method comprising:

receiving a query vector specifying a searching set of fuzzy attribute values for a collection of data;

calculating mutual subsethood measures between said query vector and a plurality of keytroids in a keytroid database, each keytroid in said keytroid database specifying a respective set of fuzzy attribute values for said collection of data; and

retrieving a subset of keytroids from said keytroid database, each keytroid in said subset of keytroids satisfying a threshold mutual subsethood measure.