Behavior Based Record Linkage

• US 20130036119A1
• Filed: 08/01/2011
• Published: 02/07/2013
• Est. Priority Date: 08/01/2011
• Status: Active Grant

• Current Assignee: Qatar Foundation
• Original Assignee: Qatar Foundation
• Inventors: YAKOUT, Mohamed, Elmagarmid, Ahmed K., Elmeleegy, Hazem, Ouzzani, Mourad, Qi, Yuan
• Granted Patent: US 9,514,167 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/24558 Binary matching operations

Abstract

A computer implemented method for matching data records from multiple entities comprising providing respective transaction logs for the entities representing actions performed by or in respect of the entities, determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

Claims

1. A computer implemented method for matching data records from multiple entities comprising:

providing respective transaction logs for the entities representing actions performed by or in respect of the entities;

determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

2. A method as claimed in claim 1, further comprising converting the transaction logs to a predetermined format to provide a processed log including data from the transaction logs and a set of identifiers representing combinations of features for respective actions.

3. A method as claimed in claim 2, further comprising generating a behavior matrix for an entity using the identifiers.

4. A method as claimed in claim 1, wherein determining a matching score includes determining if a merged entity exhibits a consistent behavior compared to a behavior pattern of actions for the individual entities.

5. A method as claimed in claim 1, wherein determining a matching score includes determining a behavior recognition score for a behavior of an entity representing consistency of the entities behavior over multiple components.

6. A method as claimed in claim 5, wherein the multiple components include a measure representing consistency in repeating actions, a measure representing stability in features describing actions, and a measure representing association between actions.

7. A method as claimed in claim 3, further comprising generating a first element of a discrete Fourier transform for a binarised behavior matrix in which non zero values are replaced with the value “

1”

to provide a complex number representing an action in the behavior matrix.

8. A method as claimed in claim 7, wherein the inverse of the magnitude of the complex number represents a recognition score for an action of an entity.

9. A method as claimed in claim 1, further comprising generating a statistical model for the behavior of an entity using the transaction log for that entity, wherein parameters of the model are used to determine a measure for repeated patterns in a sequence corresponding to action occurrences for an entity.

10. A computer program embedded on a non-transitory tangible computer readable storage medium, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities comprising:

providing respective transaction logs for the entities representing actions performed by or in respect of the entities;

determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

11. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 10, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities further comprising converting the transaction logs to a predetermined format to provide a processed log including data from the transaction logs and a set of identifiers representing combinations of features for respective actions.

12. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 11, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities further comprising generating a behavior matrix for an entity using the identifiers.

13. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 10, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities wherein determining a matching score includes determining if a merged entity exhibits a consistent behavior compared to a behavior pattern of actions for the individual entities.

14. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 10, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities wherein determining a matching score includes determining a behavior recognition score for a behavior of an entity representing consistency of the entities behavior over multiple components.

15. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 14, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities wherein the multiple components include a measure representing consistency in repeating actions, a measure representing stability in features describing actions, and a measure representing association between actions.

16. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 12, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities further comprising generating a first element of a discrete Fourier transform for a binarised behavior matrix in which non zero values are replaced with the value “

1”

to provide a complex number representing an action in the behavior matrix.

17. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 16, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities, wherein the inverse of the magnitude of the complex number represents a recognition score for an action of an entity.

18. A computer program embedded on a non-transitory tangible computer readable storage medium as claimed in claim 10, the computer program including machine readable instructions that, when executed by a processor, implement a method for matching data records from multiple entities further comprising generating a statistical model for the behavior of an entity using the transaction log for that entity, wherein parameters of the model are used to determine a measure for repeated patterns in a sequence corresponding to action occurrences for an entity.

19. A method for matching records from multiple sources, comprising:

determining a coarse match for records using a value representing a period of occurrence of certain actions for an entity including data from a merged pair of sources;

for a match above a predetermined threshold, using a statistical model to determine a final matching score.