Detecting and tracking new events/classes of documents in a data base

• US 6,804,688 B2
• Filed: 04/27/2001
• Issued: 10/12/2004
• Est. Priority Date: 05/02/2000
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Malassis, Loic, Piperakis, Romanos, Kobayashi, Mei
• Primary Examiner(s): Corrielus, Jean M.
• Assistant Examiner(s): HWANG, JOON H
• Application Number:
  US09/844,724
• Publication Number:
  US 20020007364A1
• Time in Patent Office: 1,264 Days
• Field of Search: 707/1-10, 707/100-103, 707/200-206
• US Class Current: 1/1
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99954 Version management

Abstract

Detecting and tracking of new events/classes of documents are disclosed. A method for detecting new events and/or classes of documents and tracking evolution thereof in a database, said new events and/or classes of said documents being added to said database, said documents including attribute data related to a temporal parameter, said method includes the steps of providing vectors of said documents related to attribute data including said temporal parameter included therein, and detecting said new events and classes and tracking the evolution thereof using said vectors.

Claims

1. A method for detecting new events and/or classes of documents and tracking evolution thereof in a database, said new events and/or classes of said documents being added to said database, said documents including attribute data related to a temporal parameter, said method comprising the steps of:

providing vectors of said documents based on attribute data that includes said temporal parameter, and

detecting said new events and/or classes of said documents and tracking evolution thereof using said vectors, wherein said tracking comprises evaluating vectors to determine whether said documents relate to new, follow-up, or old events and/or new, follow-up, or old classes.

2. The method according to claim 1, wherein said attribute data include at least one keyword, and said keyword is weighted with respect to a frequency of appearance in said document.

3. The method according to claim 2, wherein said temporal parameter is further weighted with respect to time elapse about a specific date, and a weight of said temporal parameter is less than the total weight of said keywords.

4. The method according to claim 1, wherein said detecting and tracking step further includes a step of providing a temporal window such that said detecting and tracking step is executed using said temporal window.

5. The method according to claim 4, wherein said temporal window is a delta function with respect to a specific date.

6. The method according to claim 4, wherein said temporal window is a symmetric Gaussian function.

7. The method according to claim 4, wherein said temporal window is a step function.

8. The method according to claim 4, wherein said temporal window is formed interactively by a user on a display window.

9. The method according to claim 4, wherein said temporal window is normalized before dimensional reduction for said vectors is carried out if the number of said keywords in each document is relatively constant in said database and the same temporal window is used for all of said documents.

10. The method according to claim 4, wherein several different temporal windows are provided so that the relative weights between said keywords and said temporal parameter becomes relatively constant from document to document if the number of said keywords in each document varies greatly.

24. The program product according to claim 2, wherein said time temporal parameter is further weighted with respect to time elapse from a specific date, and a weight of said temporal parameter is less than the total weight of said keywords.

11. A computer system including a database to which new events and/or classes of documents are added, said documents including data related to a temporal parameter, for detecting new events and/or classes of said documents and tracking evolution thereof, comprising:

means for providing vectors of said documents based on attribute data including said temporal parameter including in said document, and

means for detecting said new events and/or classes of said documents and tracking evolution thereof using said vectors, wherein said tracking comprises evaluating vectors to determine whether said documents relate to new, follow-up, or old events and/or new, follow-up, or old classes.

12. The computer system according to claim 11, wherein said attribute data include at least one keyword, and said keyword is weighted with respect to a frequency of appearance in said documents.

13. The computer system according to claim 12, wherein said temporal parameter is further weighted with respect to time elapse about a specific date, and weight of said temporal parameter is less than the total weight of said keywords.

14. The computer system according to claim 11, wherein said detecting and tracking means further includes means for providing a temporal window such that said temporal window is used by said detecting and tracking means.

15. The computer system according to claim 14, wherein said temporal window is a delta function with respect to a specific date.

16. The computer system according to claim 14, wherein said temporal window is a symmetric Gaussian function.

17. The computer system according to claim 14, wherein said temporal window is a step function.

18. The computer system according to claim 14, wherein said temporal window is formed interactively by a user on a display window.

19. The computer system according to claim 14, wherein said temporal window is normalized before dimensional reduction for said vectors is carried out if the number of said keywords in each document is relatively constant in said database and the same temporal window is used for all of said documents.

20. The computer system according to claim 14, wherein several different temporal windows are provided so that the relative weights between said keywords and said temporal parameter becomes relatively constant from document to document if the number of said keywords in each document varies greatly.

21. The computer system according to claim 11, wherein said computer system comprises a server and at least one client, and said detection and tracking step is requested from at least one client computer which transmits to said server and receives a result from said detection and tracking step.

22. A program product for detecting new events and/or classes of documents and tracking evolution thereof in a database, said new events and/or classes of said documents being added to said database, said documents including attribute data related to a temporal parameter, said method comprising steps of:

providing vectors of said documents based on attribute data that includes said temporal parameter, and

detecting said new events and/or classes of said documents and tracking evolution thereof using said vectors, wherein said tracking comprises evaluating vectors to determine whether said documents relate to new, follow-up, or old events and/or new, follow-up or old classes.

23. The program product according to claim 22, wherein said attribute data include at least one keyword, and said keyword is weighted with respect to a frequency of appearance in said documents.

25. The program product according to claim 22, wherein said detecting and tracking step further includes a step of providing a temporal window such that said detecting and tracking step is executed using said temporal window.

26. The program product according to claim 25, wherein said temporal window is a delta function with respect to a specific date.

27. The program product according to claim 25, wherein said temporal window is a symmetric Gaussian function.

28. The program product according to claim 25, wherein said temporal window is a step function.

29. The program product according to claim 25, wherein said temporal window is formed interactively by a user on a display window.

30. The program product according to claim 25, wherein said temporal window is normalized before dimensional reduction for said vectors is carried out if the number of said keywords in each document is relatively constant in said database and the same temporal window is used for all of said documents.

31. The program product according to claim 25, wherein several different temporal windows are provided so that the relative weight between said keywords and said temporal parameter becomes relatively constant from document to document it the number of said keywords in each document varies greatly.