Methods, systems, and computer program products for gathering information and statistics from a community of nodes in a network

• US 7,844,663 B2
• Filed: 07/10/2006
• Issued: 11/30/2010
• Est. Priority Date: 07/10/2006
• Status: Active Grant

• Current Assignee: Activision Publishing Incorporated (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Boutboul, Irwin
• Primary Examiner(s): Backer, Firmin
• Assistant Examiner(s): Eskandarnia, Arvin
• Application Number:
  US11/456,373
• Publication Number:
  US 20080010247A1
• Time in Patent Office: 1,604 Days
• Field of Search: 709/204
• US Class Current: 709/204
• CPC Class Codes: G06F 16/27 Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor

Abstract

A method, system, and computer program product for gathering information and statistics from a community of agent nodes in a network is provided. The method includes generating a process request. The process request includes a rule for discovering each of the agent nodes in the community and a rule for accessing local information stored on each of the agent nodes. The local information represents activities conducted by end users at each of the agent nodes. The process request also includes a set of rules for classifying data comprising the local information and developing a cumulative profile resulting from the classifying. The cumulative profile includes generic information that is descriptive of collective activities conducted by the end users and is absent information that personally identifies any of the end users.

Claims

1. A method for gathering information and statistics from a community of agent nodes in a network, comprising:

generating a process request, including;

a rule for discovering each of the agent nodes in the community;

a rule for accessing local information stored on each of the agent nodes, the local information representing at least activities conducted by end users at each of the agent nodes; and

a set of rules operable for;

classifying data comprising the local information at each of the agent nodes; and

developing a cumulative profile resulting from the classifying, the cumulative profile including generic information that is descriptive of collective activities conducted by the end users;

the cumulative profile devoid of information that personally identifies any of the end users;

transmitting the process request to a first agent node in the community; and

preventing extraction of specified information at any location in a transmission path followed by the process request among the agent nodes, the preventing including;

pre-populating the process request with noisy data prior to transmission; and

removing the noisy data from the process request when the cumulative profile is returned from the last agent node in the community.

2. The method of claim 1, further comprising:

securing the identity of the end users by pre-populating the noisy data, the noisy data comprising false, incorrect, and irrelevant data.

3. The method of claim 1, wherein the set of rules includes a learning algorithm, the learning algorithm implemented by a decision tree generated via a specified classifier, wherein nodes in the decision tree are represented by rules for analyzing and classifying selected data comprising the local information stored in each of the agent nodes in the community.

4. The method of claim 3, wherein the cumulative profile is modified via the learning algorithm at each of the agent nodes, the modification based upon the local information gathered and analyzed at each of the agent nodes in the community.

5. The method of claim 1, wherein the community of agent nodes is a grid-implemented community.

6. The method of claim 1, wherein the community of agent nodes is implemented via a peer-to-peer network.

7. A system for gathering information and statistics from a community of agent nodes in a network, comprising:

a computer processing device; and

a process request application executing on the computer processing device, the process request application implementing a method, comprising;

generating a process request, including;

a rule for discovering each of the agent nodes in the community;

a rule for accessing local information stored on each of the agent nodes, the local information representing at least activities conducted by end users at each of the agent nodes; and

a set of rules operable for;

classifying data comprising the local information at each of the agent nodes; and

developing a cumulative profile resulting from the classifying, the cumulative profile including generic information that is descriptive of collective activities conducted by the end users the cumulative profile devoid of information that personally identifies any of the end users;

transmitting the process request to a first agent node in the community; and

preventing extraction of specified information at any location in a transmission path followed by the process request among the agent nodes, the preventing including;

pre-populating the process request with noisy data prior to transmission; and

removing the noisy data from the process request when the cumulative profile is returned from the last agent node in the community.

8. The system of claim 7, wherein the method further comprises:

securing the identity of the end users by pre-populating the noisy data, the noisy data comprising false, incorrect, and irrelevant data.

9. The system of claim 7, wherein the set of rules includes a learning algorithm, the learning algorithm implemented by a decision tree generated via a specified classifier, wherein nodes in the decision tree are represented by rules for analyzing and classifying selected data comprising the local information stored in each of the agent nodes in the community.

10. The system of claim 9, wherein the cumulative profile is modified via the learning algorithm at each of the agent nodes, the modification based upon the local information gathered and analyzed at each of the agent nodes in the community.

11. The system of claim 7, wherein the community of agent nodes is a grid-implemented community.

12. The system of claim 7, wherein the community of agent nodes is implemented via a peer-to-peer network.

13. A computer program product comprising a storage medium storing instructions, which when executed by a computer implement a service for gathering information and statistics from a community of agent nodes in a network, the computer program product implementing a method, the method comprising:

generating a process request, including;

a rule for discovering each of the agent nodes in the community;

a rule for accessing local information stored on each of the agent nodes, the local information representing at least activities conducted by end users at each of the agent nodes; and

a set of rules operable for;

classifying data comprising the local information at each of the agent nodes; and

developing a cumulative profile resulting from the classifying, the cumulative profile including generic information that is descriptive of collective activities conducted by the end users;

the cumulative profile devoid of information that personally identifies any of the end users;

transmitting the process request to a first agent node in the community; and

preventing extraction of specified information at any location in a transmission path followed by the process request among the agent nodes, the preventing including;

pre-populating the process request with noisy data prior to transmission; and

removing the noisy data from the process request when the cumulative profile is returned from the last agent node in the community.

14. The computer program product of claim 13, wherein the method further comprises:

securing the identity of the end users by pre-populating the noisy data, the noisy data comprising false incorrect and irrelevant data.

15. The computer program product of claim 13, wherein the set of rules includes a learning algorithm, the learning algorithm implemented by a decision tree generated via a specified classifier, wherein nodes in the decision tree are represented by rules for analyzing and classifying selected data comprising the local information stored in each of the agent nodes in the community.

16. The computer program product of claim 15, wherein the cumulative profile is modified via the learning algorithm at each of the agent nodes, the modification based upon the local information gathered and analyzed at each of the agent nodes in the community.

17. The computer program product of claim 13, wherein the community of agent nodes is at least one of a:

grid-implemented community; and

a peer-to-peer network.

18. A service for gathering information and statistics from a community of agent nodes in a network, comprising:

generating a process request, including;

a rule for discovering each of the agent nodes in the community;

a rule for accessing local information stored on each of the agent nodes, the local information representing at least activities conducted by end users at each of the agent nodes; and

a set of rules operable for;

classifying data comprising the local information at each of the agent nodes; and

developing a cumulative profile resulting from the classifying, the cumulative profile including generic information that is descriptive of collective activities conducted by the end users;

the cumulative profile devoid of information that personally identifies any of the end users; and

transmitting the process request to a first agent node in the community; and

preventing extraction of specified information at any location in a transmission path followed by the process request among the agent nodes, the preventing including;

pre-populating the process request with noisy data prior to transmission; and

removing the noisy data from the process request when the cumulative profile is returned from the last agent node in the community.

19. The service of claim 18, wherein the set of rules includes a learning algorithm, the learning algorithm implemented by a decision tree generated via a specified classifier, wherein nodes in the decision tree are represented by rules for analyzing and classifying selected data comprising the local information stored in each of the agent nodes in the community.

20. The service of claim 18, wherein the cumulative profile is modified via the learning algorithm at each of the agent nodes, the modification based upon the local information gathered and analyzed at each of the agent nodes in the community.