ONLINE AND DISTRIBUTED OPTIMIZATION FRAMEWORK FOR WIRELESS ANALYTICS

• US 20130172041A1
• Filed: 01/19/2012
• Published: 07/04/2013
• Est. Priority Date: 12/31/2011
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: GARG, VIKAS KUMAR, Narang, Ankur, Kalyanaraman, Shivkumar
• Granted Patent: US 9,603,033 B2
• Time in Patent Office: Days
• US Class Current: 455/524
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: H04L 43/028 by filtering
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 28/16 Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]

Abstract

A method, computer program product, and computer system directed to an online and distributed optimization framework for wireless analytics. A radio network controller determines a ranking for each of a plurality of received objects using a plurality of similarity graphs. The radio network controller extracts a common structure by collaborative filtering data associated with a plurality of user devices and the plurality of received objects. The common structure is analyzed to infer usage patterns within a time slot. The radio network controller stores a subset of the ranked objects of the plurality of received objects in response to the analysis.

Claims

1-8. -8. (canceled)

9. A computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

determining a ranking for each of a plurality of received objects using a plurality of similarity graphs;

extracting a common structure by collaborative filtering data associated with a plurality of user devices and the plurality of received objects;

analyzing the common structure to infer usage patterns within a time slot; and

storing a subset of the ranked objects of the plurality of received objects in response to the analysis.

10. The computer program product of claim 9 wherein the plurality of similarity graphs include an object-object similarity graph, a base station-base station similarity graph, and a user-user similarity graph.

11. The computer program product of claim 9 wherein the ranking for each of the plurality of received objects is based upon, at least in part, a demand at each of a plurality of base stations, a weighting factor for premium and non-premium demands, and a bandwidth associated with each object of the plurality of received objects.

12. The computer program product of claim 9 wherein storing a subset of the ranked objects of the plurality of received objects further comprises cooperatively storing the subset of higher ranked objects across a plurality of base stations.

13. The computer program product of claim 9 further comprising generating demand predictions across time slots for a plurality of base stations.

14. The computer program product of claim 9 further comprising identifying which of the plurality of user devices and the plurality of received objects appear together in the plurality of similarity graphs.

15. The computer program product of claim 9 further comprising generating an online update of the ranking for each of the plurality of received objects and the plurality of users.

16. The computer program product of claim 9 wherein the collaborative filtering is based upon, at least in part, co-clustering.

17. A computing system including a processor and memory configured to perform operations comprising:

determining a ranking for each of a plurality of received objects using a plurality of similarity graphs;

extracting a common structure by collaborative filtering data associated with a plurality of user devices and the plurality of received objects;

analyzing the common structure to infer usage patterns within a time slot; and

storing a subset of the ranked objects of the plurality of received objects in response to the analysis.

18. The computing system of claim 17 wherein the plurality of similarity graphs include an object-object similarity graph, a base station-base station similarity graph, and a user-user similarity graph.

19. The computing system of claim 17 wherein the ranking for each of the plurality of received objects is based upon, at least in part, a demand at each of a plurality of base stations, a weighting factor for premium and non-premium demands, and a bandwidth associated with each object of the plurality of received objects.

20. The computing system of claim 17 wherein storing a subset of the ranked objects of the plurality of received objects further comprises cooperatively storing the subset of higher ranked objects across a plurality of base stations.

21. The computing system of claim 17 further comprising generating demand predictions across time slots for a plurality of base stations.

22. The computing system of claim 17 further comprising identifying which of the plurality of user devices and the plurality of received objects appear together in the plurality of similarity graphs.

23. The computing system of claim 17 further comprising generating an online update of the ranking for each of the plurality of received objects and the plurality of users.

24. The computing system of claim 17 wherein the collaborative filtering is based upon, at least in part, co-clustering.