ONLINE AND DISTRIBUTED OPTIMIZATION FRAMEWORK FOR WIRELESS ANALYTICS

• US 20130172043A1
• Filed: 07/13/2012
• 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: Narang, Ankur, Kalyanaraman, Shivkumar, Garg, Vikas Kumar
• Granted Patent: US 9,622,091 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. A computer-implemented method comprising:

determining, by a radio network controller, a ranking for each of a plurality of received objects using a plurality of similarity graphs;

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

analyzing, by the radio network controller, the common structure to infer usage patterns within a time slot; and

storing, by the radio network controller, a subset of the ranked objects of the plurality of received objects in response to the analysis.

2. The computer-implemented method of claim 1 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.

3. The computer-implemented method of claim 1 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.

4. The computer-implemented method of claim 1 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.

5. The computer-implemented method of claim 1 further comprising generating demand predictions across time slots for a plurality of base stations.

6. The computer-implemented method of claim 1 further comprising identifying which of the plurality of user devices and the plurality of received objects appear together in the plurality of similarity graphs.

7. The computer-implemented method of claim 1 further comprising generating an online update of the ranking for each of the plurality of received objects and the plurality of users.

8. The computer-implemented method of claim 1 wherein the collaborative filtering is based upon, at least in part, co-clustering.

9-24. -24. (canceled)