System and method for discovering correlations among data

• US 20060167825A1
• Filed: 01/24/2005
• Published: 07/27/2006
• Est. Priority Date: 01/24/2005
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Sayal, Mehmet
• Application Number:
  US11/041,539
• Publication Number:
  US 20060167825A1
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Embodiments of the present invention relate to a system and method for discovering correlations among data. Embodiments of the present invention comprise detecting change points in time-series data streams, defining change point properties based on the change points, grouping together two time-series data streams that have a similar change point property, calculating a behavior index for the two time-series data streams, and assigning the two time-series data streams to a server taking into account the behavior index.

Claims

1. A method for discovering correlations among data, comprising:

detecting change points in time-series data streams;

defining change point properties based on the change points;

grouping together two time-series data streams that have a similar change point property;

calculating a behavior index for the two time-series data streams; and

assigning the two time-series data streams to a server taking into account the behavior index.

2. The method of claim 1, further comprising:

determining a time distance for which a confidence of time-correlation is high for the two time-series data streams; and

generating a time-correlation rule from the time distance.

3. The method of claim 1, further comprising summarizing the two time-series data streams.

4. The method of claim 1, further comprising using parallel and distributed algorithms to provide distribution of the two time-series data streams among a plurality of servers.

5. The method of claim 1, further comprising detecting trend changes in the time-series data streams using a CUSUM function.

6. The method of claim 1, further comprising refreshing the time-series data streams using an aging mechanism.

7. The method of claim 1, further comprising defining a direction for a one of the change points as a change point property.

8. The method of claim 1, further comprising defining a count of change points in a one of the time-series data streams as a change point property.

9. The method of claim 1, further comprising defining a magnitude of change as a change point property.

10. The method of claim 1, further comprising:

recording all change points into a single time-series data stream; and

synchronizing access to the single time-series data stream using constructs for synchronization and mutual exclusion such that only a single server can access the single time-series data stream at a time.

11. The method of claim 1, further comprising:

recording the change points to create change point records; and

distributing the change point records among available servers such that similar time-series data streams are at the same server.

12. A method for discovering correlations among data, comprising:

detecting change points in time-series data streams;

defining a set of change point properties;

forming a time-series data group from the time-series data streams, wherein the time-series data group includes time-series data streams having similar change point properties; and

assigning the time-series data group to a server using an algorithm based on a type of computing environment in which the server resides.

13. The method of claim 12, further comprising calculating a behavior index and using the behavior index with the algorithm to assign the time-series data group.

14. The method of claim 12, wherein the algorithm is a parallel algorithm.

15. The method of claim 12, further comprising determining a time distance value for which a time-correlation meets a threshold value for the time-series data group.

16. The method of claim 15, further comprising generating a time-correlation rule from the time distance.

17. The method of claim 12, further comprising refreshing the time-series data streams using an aging mechanism.

18. A system for discovering correlations among data, comprising:

a change point detection module adapted to detect change points in time-series data streams;

a property module adapted to define a set of change point properties;

a grouping module adapted to form a time-series data group from the time-series data streams, wherein the time-series data group includes time-series data streams having similar change point properties;

a behavior index module adapted to calculate a behavior index for the time-series data group; and

an assigning module adapted to assign the time-series data group to a server using the behavior index.

19. The system of claim 18, further comprising:

a time distance module adapted to determine a time distance for which a confidence of time-correlation is high for the time-series data group; and

a rule module adapted to generate a time-correlation rule based on the time distance.

20. The system of claim 18, further comprising a time granularity module adapted to summarize the time-series data streams at different time granularities.

21. Application instructions on a computer-usable medium where the instructions, when executed, effect discovering correlations among data, comprising:

a change point detection module adapted to detect change points in time-series data streams;

a property module adapted to define a set of change point properties;

a grouping module adapted to form a time-series data group from the time-series data streams, wherein the time-series data group includes time-series data streams having similar change point properties;

a behavior index module adapted to calculate a behavior index for the time-series data group; and

an assigning module adapted to assign the time-series data group to a server using the behavior index.

22. The application instructions of claim 21, further comprising a summarization module adapted to summarize the time-series data streams.

23. The application instructions of claim 21, further comprising a time distance module adapted to determine a time distance for which a confidence of time-correlation is high for the time-series data group.

24. The application instructions of claim 23, further comprising a rule module adapted to generate a time-correlation rule based on the time distance.

25. The application instructions of claim 21, further comprising a time granularity module adapted to summarize the time-series data streams at different time granularities.

26. A system for discovering correlations among data, comprising:

means for detecting change points in time-series data streams;

means for defining change point properties using the change points;

means for grouping together two of the time-series data streams having a similar change point property;

means for calculating a behavior index for the two time-series data streams; and

means for assigning the two time-series data streams to a server using the behavior index.

27. A method for discovering correlations among data, comprising:

detecting change points in time-series data streams;

defining a set of change point properties;

forming a time-series data group from the time-series data streams, wherein the time-series data group includes time-series data streams having similar change point properties;

assigning the time-series data group to a server using an algorithm using a type of computing environment in which the server resides;

calculating a behavior index and using the behavior index with the algorithm to assign the time-series data group;

determining a time distance value for which a time-correlation meets a threshold value for the time-series data group;

generating a time-correlation rule using the time distance; and

refreshing the time-series data streams using an aging mechanism.