Multi-Source, Multi-Dimensional, Cross-Entity, Multimedia Analytical Model Sharing Database Platform Apparatuses, Methods and Systems

• US 20130204894A1
• Filed: 02/04/2013
• Published: 08/08/2013
• Est. Priority Date: 02/02/2012
• Status: Active Grant

• Current Assignee: Visa International Service Association (Visa, Inc.)
• Original Assignee: Visa International Service Association (Visa, Inc.)
• Inventors: Faith, Patrick, Harris, Theodore David
• Granted Patent: US 10,013,423 B2
• Time in Patent Office: Days
• US Class Current: 707/769
• CPC Class Codes: G06F 16/164 File meta data generation
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass

Abstract

The MULTI-SOURCE, MULTI-DIMENSIONAL, CROSS-ENTITY, MULTIMEDIA ANALYTICAL MODEL SHARING DATABASE PLATFORM APPARATUSES, METHODS AND SYSTEMS (“AMSDB”) transform data aggregated from various computer resources using AMSDB components into updated entity profiles and/or social graphs. In one implementation, the AMSDB aggregates data records including search results, purchase transaction data, service usage data, service enrollment data, and social data. The AMSDB identifies data field types within the data records and their associated data values. From the data field types and their associated data values, the AMSDB identifies an entity. The AMSDB generates correlations of the entity to other entities identifiable from the data field types and their associated data values. The AMSDB also associates attributes to the entity by drawing inferences related to the entity from the data field types and their associated data values. Using the generated correlations and associated attributes, the AMSDB generates an updated profile and social graph of the entity. The AMSDB provides the updated profile and social graph for an automated web form filling request.

Claims

1. An analytical model sharing processor-implemented method for privacy enhanced analytical model sharing through the use of contextual privacy dataset modifications, comprising:

obtaining a request to publish an analytical model operating on user data retrieved from a distributed linking node mesh;

determining whether the analytical model includes a model dataset reference;

querying a dataset database for a dataset corresponding to the model dataset reference;

determining that the dataset corresponding to the model dataset reference is available for use by the analytical model; and

publishing the analytical model to an analytical model sharing service.

2. The method of claim 1, additionally comprising:

determining that the analytical model requires a dataset containing personally identifiable consumer data; and

applying at least one data anonymization operation to the personally identifiable consumer data.

3. The method of claim 2, wherein the anonymization operation is a masking operation applied to the personally identifiable consumer data.

4. The method of claim 2, wherein the anonymization operation is a hash function.

5. The method of claim 4, wherein the hash function is MD5.

6. The method of claim 4, wherein the hash function is HAVAL.

7. The method of claim 2, wherein the anonymization operation is the creation of a dictionary key containing a reduced set of the personally identifiable consumer data.

8. The method of claim 7, additionally comprising storing the dictionary key in a distributed linking node mesh.

9. The method of claim 1, wherein the request to publish an analytical model contains a user generated analytical model.

10. The method of claim 9, wherein the user generated analytical model contains contextless meta-data.

11. The method of claim 10, wherein the contextless meta-data is extensible markup language.

12. The method of claim 10, wherein the user generated analytical model contains interpreted code.

13. The method of claim 12, wherein the interpreted code is Python.

14. The method of claim 12, wherein the interpreted code is structured query language statements.

15. The method of claim 12, wherein the interpreted code is PHP.

16. The method of claim 9, additionally comprising:

converting the user generated analytical model into a model containing contextless meta-data.

17. The method of claim 16, wherein converting the user generated analytical model comprises:

determining a language definition corresponding to the language of the user generated analytical model;

creating a mapping between the language definition and commands contained within the user generated analytical model; and

applying a transformation, using the mapping, to the user generated analytical model.

18. The method of claim 17, wherein the transformation is a custom function containing a plurality of operations in the language of the user generated analytical model.

19. The method of claim 17, wherein creating the mapping includes performing a regular expression search of the user generated analytical model.

20. An analytical model sharing processor-implemented method for privacy enhanced analytical model sharing through the use of contextual privacy dataset modifications, comprising:

obtaining a request to publish an analytical model operating on user data retrieved from a distributed linking node mesh, wherein the request includes an analytical model expressed as contextless metadata;

determining whether the analytical model includes a model dataset reference;

querying a database for a dataset corresponding to the model dataset reference;

determining that the dataset contains personally identifiable consumer data;

querying an anonymizing database for at least one data anonymization operation;

applying the at least one data anonymization operation to the personally identifiable consumer data;

storing a modified dataset containing the anonymized consumer data;

updating the analytical model to include a reference to the modified dataset; and

publishing the analytical to an analytical model sharing service.