METHOD AND SYSTEM FOR DETECTING CORRELATION IN DATA SETS

• US 20110060703A1
• Filed: 09/04/2009
• Published: 03/10/2011
• Est. Priority Date: 09/04/2009
• Status: Active Grant

• Current Assignee: Alex Alaniz
• Original Assignee: Alex Alaniz
• Inventors: ALANIZ, ALEX
• Granted Patent: US 8,229,866 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18
• CPC Class Codes: G06F 18/2135 based on approximation criteria, e.g. principal component analysis
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method and system for detecting correlations in a data set is provided. The method includes determining one or more parameters associated with one or more data sets. The one or more parameters are determined at runtime for generating one or more test data sets from the one or more data sets. A test data of the one or more test data sets comprises one or more objects and one or more indices. The one or more objects are associated with the one or more indices. The method further includes computing one or more correlation coefficients associated with the one or more test data sets. The one or more correlation coefficients are computed for detecting correlation corresponding to the one or more test data sets.

Claims

1. A system for analyzing at least one data set, wherein a data set of the at least one data set comprises at least one object, the system comprising:

a memory for storing the at least one data set; and

a processor coupled to the memory, wherein the processor is configured to;

determine at least one parameter associated with the at least one data set at runtime, a parameter of the at least one parameter is one of a context, a weight, a time period, a strength and a relevance;

generate at least one test data set from the at least one data set at runtime in response to determining the at least one parameter, a lest data set of the at least one test data set comprises at least one object of the at least one data set and at least one index, wherein the at least one object of the at least one test data set is associated with the at least one index;

compute at least one correlation coefficient at runtime in response to generating the at least one test data set; and

detect correlation corresponding to the at least one object of the at least one test data set based on the at least one correlation coefficient.

2. The system of claim 1, wherein the at least one data set is periodically updated.

3. The system of claim 1, wherein an index of the at least one index is one of a spatial index and a temporal index.

4. The system of claim 1, wherein the processor is further configured to fuse information associated with the at least one data set stored in the memory.

5. The system of claim 1, wherein the processor is further configured to reduce the at least one test data set to a canonical form in response to generating the at least one test data set.

6. The system of claim 1, wherein the processor is further configured to transform the at least one test data set into a function space in response to generating the at least one test data set.

7. The system of claim 6, wherein the processor is further configured to calculate at least one lag corresponding to the at least one test data set, the at least one lag corresponds to the at least one object of the at least one test data set, a lag of the at least one lag is one of a phase lag and a time lag.

8. The system of claim 1, wherein the processor is further configured to construct at least one forecasting model based on the correlation, wherein a forecasting model of the at least one forecasting model is associated with the at least one object of the at least one test data set.

9. The system of claim 8, wherein the processor is further configured to:

compute an error associated with the forecasting model; and

compare the error with a threshold error value.

10. The system of claim 9, wherein the processor is further configured to modify the at least one correlation coefficient based on the comparison between the error and the threshold error value.

11. The system of claim 10, wherein the processor is further configured to perform at least one of:

refine the at least one test data set; and

re-detect correlations in the at least one test data set.

12. A computer program product comprising a computer usable medium having a computer readable program for facilitating a method for analyzing at least one data set, wherein a data set of the at least one data set comprises at least one object, the method comprising:

determining at least one parameter associated with the at least one data set at runtime, a parameter of the at least one parameter is one of a context, a weight, a time period, a strength and a relevance;

generating at least one test data set from the at least one data set at runtime in response to determining the at least one parameter, a test data set of the at least one test data set comprises at least one object of the at least one data set and at least one index, wherein the at least one object of the at least one test data set is associated with the at least one index;

computing at least one correlation coefficient at runtime in response to generating the at least one test data set; and

detecting a correlation corresponding to the at least one object of the at least one test data set based on the at least one correlation coefficient.

13. The computer program product claim 12, wherein an index of the at least one index is one of a spatial index and a temporal index.

14. The computer program product of claim 12, wherein generating the at least one test data set at runtime comprises fusing information associated with at least one data set stored in the computer useable medium.

15. The computer program product of claim 12 further comprising reducing the at least one test data set to a canonical form in response to generating the at least one test data set.

16. The computer program product of claim 12 further comprising transforming the at least one test data set into a function space in response to generating the at least one test data set.

17. The computer program product of claim 16 further comprising calculating at least one lag corresponding to the at least one test data set, the at least one lag corresponds to the at least one object of the at least one test data set, a lag of the at least one lag is one of a phase lag and a time lag.

18. The computer program product of claim 12 further comprising constructing at least one forecasting model based on the correlation, wherein a forecasting model of the at least one forecasting model is associated with the at least one object of the at least one test data set.

19. The computer program product of claim 18, wherein the forecasting model is a function of at least one of a correlation coefficient of the at least one correlation coefficient, an index of the at least one index and a lag of the at least one lag corresponding to the at least one test data set, the lag of the at least one lag is associated with the at least one object corresponds to the at least one test data set.

20. The computer program product of claim 19 further comprising:

computing an error associated with the forecasting model; and

comparing the error with a threshold error value.

21. The computer program product of claim 20 further comprising modifying the correlation coefficient based on the comparison between the error and the threshold error value.

22. The computer program product of claim 21 further comprising:

refining the at least one test data set; and

re-detecting correlations in the at least one test data set.