System and method for measuring the quality of document sets

• US 8,874,549 B2
• Filed: 06/25/2008
• Issued: 10/28/2014
• Est. Priority Date: 06/26/2007
• Status: Active Grant

• Current Assignee: Oracle OTC Subsidiary LLC (Oracle Corporation)
• Original Assignee: Oracle OTC Subsidiary LLC (Oracle Corporation)
• Inventors: Tunkelang, Daniel, Wang, Joyce Jeanpin, Zelevinsky, Vladimir
• Primary Examiner(s): Alam, Hosain
• Assistant Examiner(s): JOHNSON, JOHNESE T
• Application Number:
  US12/146,267
• Publication Number:
  US 20090006386A1
• Time in Patent Office: 2,316 Days
• Field of Search: 707/722, 707/737, 707/777, 707/723, 707/736, 707/738, 707/759, 707/803, 707/769
• US Class Current: 707/722
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/3331 Query processing

Abstract

Systems and methods are described that calculate the interestingness of a set of one or more records in a database, either absolutely (i.e., compared to an overall collection of records) or relative to some other set of records. In one embodiment, the measure is a relative entropy value that has been normalized. Various applications of the measure are described in the context of an information retrieval system. These applications include, for example, guiding query interpretation, guiding view selection and summarization, intelligent ranges, event detection, concept triggers and interpreting user actions, hierarchy discovery, and adaptive data mining.

Claims

1. A computer implemented method for presenting a view of a result obtained from interaction with a collection of information, the method comprising:

accessing, by a computer system, at least one result set in response to interaction with a collection of information;

determining, by the computer system, at least one identifying characteristic within the at least one result set returned from the interaction with a collection of information;

determining, by the computer system, a statistical distribution of the at least one identifying characteristic within the at least one result set;

generating, by the computer system, a measurement of distinctiveness for the at least one result set based, at least in part, on the statistical distribution of the at least one identifying characteristic within the at least one result set, wherein the distinctiveness of the at least one result set is measured in relation to the collection of information, and wherein the generating comprises determining the measurement of distinctiveness from a statistical distribution of at least one identifying characteristic in the at least one result set against a baseline statistical distribution, and wherein the baseline statistical distribution is determined at a time of the interaction or thereafter;

modifying, by the computer system, the at least one result set based at least in part on the measurement of distinctiveness for the at least one result set; and

returning the modified result set;

wherein the modifying the at least one result set comprises determining a contribution of the at least one identifying characteristic to the measurement of distinctiveness and highlighting the at least one identifying characteristic within the at least one result set based on the determined contribution.

2. The method according to claim 1, further comprising approximating the statistical distribution of at least one identifying characteristic within the at least one result set.

3. The method according to claim 1, wherein the generating the measurement of distinctiveness comprises assigning a weight value associated with at least one element of the at least one result set.

4. The method according to claim 1, wherein the generating the measurement of distinctiveness comprises normalizing the measurement of distinctiveness, wherein the normalizing the measurement of distinctiveness comprises performing a computation on the measurement of distinctiveness that accounts for a size of any of the at least one result set.

5. The method according to claim 1, wherein the generating a measurement of distinctiveness comprises:

modifying a size of a set;

generating a first sampled set from the result set; and

generating the measurement of distinctiveness against the modified set.

6. The method according to claim 1, wherein the modifying the at least one result is further based on determining a contribution of a respective one of the at least one identifying characteristic of the at least one result to the measure of distinctiveness.

7. The method according to claim 1, wherein the modifying the result comprises reducing the volume of information presented from the at least one result.

8. The method according to claim 1, further comprising clustering members of the at least one result set based on the measurement of distinctiveness.

9. The method according to claim 8, further comprising identifying a subset of elements within a cluster of members of at least one cluster, wherein the subset of elements is selected based on identifying characteristics shared with the cluster.

10. The method according to claim 8, wherein the modifying the at least one result set based on the statistical distribution of the at least one identifying characteristic further comprises outputting the result set organized by at least one cluster of elements.

11. The method according to claim 9, wherein the modifying the at least one result set based on the statistical distribution of the at least one identifying characteristic further comprises outputting the representative elements within the cluster of elements.

12. The method according to claim 9, wherein the modifying the at least one result set based on the statistical distribution of the at least one identifying characteristic further comprises reducing the at least one result set to a set of the representative elements within the cluster of elements.

13. The method according to claim 8, further comprising providing for selection of at least one cluster within the modified result set.

14. The method according to claim 1, wherein the modifying further comprises ranking at least one element of the at least one result set.

15. The method according to claim 1, wherein the modifying further comprises filtering at least one element of the at least one result set.

16. The method according to claim 14, wherein the ranking further identifies a value of the at least one element against at least one other element of the result set.

17. The method according to claim 15, wherein the filtering further identifies a value of the at least one element against at least one other element of the result set.

18. The method according to 1, further comprising:

receiving a selection associated with the modified result; and

refining the modified result in response to the received selection.

19. The method according to 18, wherein the refining comprises an act of recalculating the measurement of distinctiveness based on the selection.

20. The method according to 19, wherein the recalculating the measurement of distinctiveness comprises modifying a weight value associated with at least one element of the at least one result set.

21. The method according to claim 19 wherein the selection comprises at least one of a selection of an identifying characteristics within the modified result, selection of a cluster within the modified result, selection of a subset of the modified result, and selection of representative elements within a cluster of elements.

44. The method according to claim 1, wherein the modifying the at least one result set comprises modifying a view of the at least one result set.

45. The method according to claim 1, wherein the generating the measurement of distinctiveness is determined by comparing the statistical distribution of the at least one identifying characteristic within the at least one result set against the statistical the at least one identifying characteristic within another set from the collection of information.

22. A non-transitory computer-readable medium having computer-readable instructions stored thereon that define instructions that, as a result of being executed by a computer, present a view of a result returned from a collection of information:

optimizing a view of at least one result set, wherein the optimizing comprises;

determining at least one identifying characteristic within the at least one result set returned in response to interaction with a collection of information;

determining a statistical distribution of the at least one identifying characteristic within the at least one result set;

measuring the distinctiveness of the at least one result set based, at least in part, on the statistical distribution of the at least one identifying characteristic within the at least one result set, wherein the distinctiveness of the at least one result set is measured in relation to the collection of information, and wherein the generating comprises determining the measurement of distinctiveness from a statistical distribution of at least one identifying characteristic in the at least one result set against a baseline statistical distribution, and wherein the baseline statistical distribution is determined at a time of the interaction or thereafter;

modifying the at least one result set based at least in part on the measure of distinctiveness; and

outputting the modified result;

wherein the modifying the at least one result set comprises determining a contribution of the at least one identifying characteristic to the measure of distinctiveness and highlighting the at least one identifying characteristic within the at least one result set based on the determined contribution.

46. The non-transitory computer-readable medium of claim 22, wherein the generating the measurement of distinctiveness comprises assigning a weight value associated with at least one element of the at least one result set.

47. The non-transitory computer-readable medium of claim 22, wherein the generating the measurement of distinctiveness comprises normalizing the measurement of distinctiveness, wherein the normalizing the measurement of distinctiveness comprises performing a computation on the measurement of distinctiveness that accounts for a size of any of the at least one result set.

23. A system for presenting an improved view of a result returned from a collection of information, the system comprising:

at least one processor operatively connected to a memory, the processor when executing provides;

an analysis engine adapted to determine at least one identifying characteristic within at least one result set returned in response to interaction with a collection of information;

a distinctiveness engine adapted to determine a measurement of the distinctiveness of at least one result set based, at least in part, on a statistical distribution of the at least one identifying characteristic within the at least one result set, wherein the distinctiveness of the at least one result set is measured in relation to the collection of information, and wherein the generating comprises determining the measurement of distinctiveness from a statistical distribution of at least one identifying characteristic in the at least one result set against a baseline statistical distribution, and wherein the baseline statistical distribution is determined at a time of the interaction or thereafter; and

a summarization engine adapted to modify the at least one result set based at least in part on the determined measurement of the distinctiveness of the result set;

wherein the summarization engine is further configured to determine a contribution of the at least one identifying characteristic to the measurement of distinctiveness and highlight the at least one identifying characteristic within the at least one result set based on the determined contribution.

24. The system according to claim 23, further comprising an approximation engine adapted to approximate the statistical distribution of at least one identifying characteristic within the at least one result set.

25. The system according to claim 23, further comprising a weighting engine adapted to assign a weight value to at least one element of the at least one result set.

26. The system according to claim 23, further comprising a normalization engine adapted to normalize the distinctiveness of the result.

27. The system according to claim 23, further comprising a generation engine adapted to modify a size of a set, and wherein the distinctiveness engine is further adapted to determine distinctiveness against the modified set.

28. The system according to claim 23, wherein the summarization engine is further adapted to modify the at least one result based on determining a contribution of an element of the at least one result to the measure of distinctiveness.

29. The system according to claim 23, wherein the summarization engine is further adapted to reduce the volume of information presented from the at least one result.

30. The system according to claim 23, wherein the summarization engine is further adapted to cluster elements within the result set based on the measurement of distinctiveness.

31. The system according to claim 30, wherein the summarization engine is further adapted to identify a subset of elements within a cluster of elements of at least one cluster, wherein the subset of elements is selected based on identifying characteristics shared with the cluster.

32. The system according to claim 30, wherein the summarization engine is further adapted to output the result set organized by at least one cluster of elements.

33. The system according to claim 31, wherein the summarization engine is further adapted to output the representative elements within the cluster of elements.

34. The system according to claim 31, wherein the summarization engine is further adapted to reduce the at least one result set to a set of the representative elements within the cluster of elements.

35. The system according to claim 30, further comprising an input engine adapted to receive a selection of at least one cluster within the modified result set.

36. The system according to claim 23, wherein the summarization engine is further adapted to rank at least one element of the at least one result set.

37. The system according to claim 23, wherein the summarization engine is further adapted to filter at least one element of the at least one result set.

38. The system according to claim 36, wherein the summarization engine is further adapted to identify a value of the at least one element against at least one other element of the result set.

39. The system according to claim 37, wherein the summarization engine is further adapted to identify a value of the at least one element against at least one other element of the result set.

40. The system according to 23, further comprising an input engine adapted to receive a selection associated with the modified result, and wherein the summarization engine is further adapted to refine the modified result in response to the received selection.

41. The system according to 40, wherein the distinctiveness engine is further adapted to recalculate the distinctiveness of the result based on the selection.

42. The system according to 41, wherein the distinctiveness engine is further adapted to modify a weight value associated with at least one element of the at least one result set.

43. The system according to claim 41, wherein the selection comprises at least one of a selection of an identifying characteristics within the modified result, selection of a cluster within the modified result, selection of a subset of the modified result, and selection of representative elements within a cluster of elements.