System, method and computer-readable medium for providing pattern matching

• US 7,627,602 B2
• Filed: 07/11/2008
• Issued: 12/01/2009
• Est. Priority Date: 08/21/2001
• Status: Expired due to Term

• Current Assignee: AT&T Intellectual Property II LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property II LP (AT&T, Inc.)
• Inventors: Muthukrishnan, Shanmugavelayutham, Indyk, Piotr, Koudas, Nikolaos
• Primary Examiner(s): LEROUX, ETIENNE PIERRE
• Application Number:
  US12/171,911
• Publication Number:
  US 20080275908A1
• Time in Patent Office: 508 Days
• Field of Search: 707/2, 707/102, 382/225
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2474 Sequence data queries, e.g. querying versioned data
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A system, method and computer-readable medium are disclosed for identifying representative data using sketches. The method embodiment comprises generating a plurality of vectors from a data set, modifying each of the vectors of the plurality of vectors and selecting one of the plurality of generated vectors according to a comparison of a summed distance between a modified vector associated with the selected generated vector and remaining modified vectors. Modifying the generated vectors may involve reduced each generated vector to a lower dimensional vector. The summed distance then represents a summed distance between the lower dimensional vector and remaining lower dimensional vectors.

Claims

1. A method causing an electronic computing device to perform steps comprising:

choosing a dimension associated with the lower dimensional vector;

generating a subvector from the higher dimensional vector;

generating a random vector;

calculating the lower dimensional vector based on the subvector and the random vector; and

identifying trends in data represented by the higher dimensional vector based on the lower dimensional vector.

2. The method of claim 1, further comprising:

normalizing the random vector.

3. The method of claim 1, wherein calculating the lower dimensional vector further comprises calculating the dot product between the subvector and the random vector.

4. The method of claim 1, wherein calculating the lower dimensional vector further comprises projecting the subvector onto the random vector.

5. The method of claim 1, wherein calculating the lower dimensional vector further comprises using a polynomial convolution between the subvector and the random vector.

6. The method of claim 1, wherein the subvector is generated by partitioning data in the higher dimensional vector.

7. The method of claim 1, wherein the random vector has the same dimension as the subvector.

8. A tangible computer-readable medium storing instructions for controlling a computing device to generate a lower dimensional vector that represents a higher dimensional vector, the instructions comprising:

choosing a dimension associated with the lower dimensional vector;

generating a subvector from the higher dimensional vector;

generating a random vector; and

calculating the lower dimensional vector based on the subvector and the random vector to identify trends in data represented by the higher dimensional vector based on the lower dimensional vector.

9. The computer-readable medium of claim 8, the instructions further comprising:

normalizing the random vector.

10. The computer-readable medium of claim 8, wherein calculating the lower dimensional vector further comprises calculating the dot product between the subvector and the random vector.

11. The computer-readable medium of claim 8, wherein calculating the lower dimensional vector further comprises projecting the subvector onto the random vector.

12. The computer-readable medium of claim 8, wherein calculating the lower dimensional vector further comprises using a polynomial convolution between the subvector and the random vector.

13. The computer-readable medium of claim 8, wherein the subvector is generated by partitioning data in the higher dimensional vector.

14. The computer-readable medium of claim 8, wherein the random vector has the same dimension as the subvector.

15. A system for generating a lower dimensional vector that represents a higher dimensional vector, the system comprising:

a processor of an electronic computing device;

a module configured to control the processor to choose a dimension associated with the lower dimensional vector;

a module configured to control the processor to generate a subvector from the higher dimensional vector;

a module configured to control the processor to generate a random vector; and

a module configured to control the processor to calculate the lower dimensional vector based on the subvector and the random vector and identify trends in the data represented by the higher dimensional vector based on the lower dimensional vector.

16. The system of claim 15, wherein the random vector has the same dimension as the subvector.

17. The system of claim 15, further comprising:

a module configured to control the processor to normalize the random vector.

18. The system of claim 15, wherein the module configured to control the processor to calculate the lower dimensional vector further calculates the dot product between the subvector and the random vector.

19. The system of claim 15, wherein the module configured to control the processor to calculate the lower dimensional vector further projects the subvector onto the random vector.

20. The system of claim 15, wherein the module configured to control the processor to calculate the lower dimensional vector further uses a polynomial convolution between the subvector and the random vector.

21. The system of claim 15, wherein the module configured to control the processor to generate the subvector further partitions data in the higher dimensional vector.

22. The system of claim 15, wherein the random vector has the same dimension as the subvector.