Method and apparatus for information clustering based on predictive social graphs

• US 9,477,787 B2
• Filed: 08/30/2011
• Issued: 10/25/2016
• Est. Priority Date: 08/30/2011
• Status: Expired due to Fees

• Current Assignee: Nokia Technologies Oy (Nokia Corporation)
• Original Assignee: Nokia Technologies Oy (Nokia Corporation)
• Inventors: Boldyrev, Sergey, Kalra, Pavandeep
• Primary Examiner(s): Saeed, Usmaan
• Assistant Examiner(s): Perez-Arroyo, Raquel
• Application Number:
  US13/221,452
• Publication Number:
  US 20130054604A1
• Time in Patent Office: 1,883 Days
• Field of Search: 707/737, 707/738, 707/740, 707/739
• US Class Current: 1/1
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/906 Clustering; Classification
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

An approach is provided for providing information clustering based on predictive social graphs. An information clustering platform processes and/or facilitates a processing of one or more social graphs associated with one or more users to cause, at least in part, a prediction of one or more future states of the one or more social graphs. The information clustering platform further causes, at least in part, a clustering of one or more data items associated with at least one information space based, at least in part, on the one or more social graphs, the one or more future states, or a combination thereof.

Claims

1. A method comprising:

processing one or more social graphs associated with one or more users to cause a prediction of one or more future states of the one or more social graphs, wherein processing the one or more social graphs comprises,

determining respective information trajectories defining a finite set of possible, nearest future states of the respective one or more social graphs,

extracting historical patterns based on the one or more social graphs, and

selecting the one or more future states from the set of possible future states based at least in part on the most recent historical patterns; and

clustering one or more data items distributed over a plurality of storage partitions in a cloud, associated with at least one information space and at least one computation space, based, at least in part, on the one or more social graphs, the one or more future states, or a combination thereof, wherein the at least one information space comprises data associated with a user of a social network and the at least one computation space comprises computation closures associated with computation resources of the cloud, based, at least in part, on processing and maintaining the one or more social graphs, wherein computation closures identify a respective computation procedure together with relations and communications among one or more processes including one or more of passing arguments, sharing process results, selecting results provided from computation of alternative inputs or flow of data.

2. A method of claim 1 further comprising:

processing one or more attributes associated with the one or more users to determine the one or more social graphs, the one or more future states, the one or more data items, or a combination thereof.

3. A method of claim 1 further comprising:

determining at least one sequence of one or more information management processes associated with the one or more data items;

processing the at least one sequence to determine one or more process states of the respective one or more information management processes;

determining one or more information state trajectories based, at least in part, on at least one sequence, the one or more process states, or a combination thereof; and

causing, the prediction of the one or more future states based, at least in part, on the one or more information state trajectories.

4. A method of claim 3 further comprising:

processing the one or more information state trajectories, the one or more future states, or a combination thereof to determine recyclability information associated with the one or more data items,

wherein the clustering of the one or more data items is based, at least in part, on the recyclability information.

5. A method of claim 3 further comprising:

determining at least one model state space based, at least in part, on the one or more state trajectories, the one or more process states, or a combination thereof; and

causing the prediction of the one or more future states based, at least in part, on an observation of the at least one model state space.

6. A method of claim 3, wherein the one or more process states include, at least in part, one or more active states, one or more inactive states, one or more transitional states, or a combination thereof, and the one or more information state trajectories include, at least in part, one or more regular motions, one or more stochastic motions, or a combination thereof with respect to the one or more process states.

7. A method of claim 1 further comprising:

determining a history of utilization of the one or more data items, the at least one information space, or a combination thereof;

processing the history to cause, at least in part, an extraction of one or more patterns; and

determining the one or more finite sets, the one or more possible future states, the one or more future states, or combination thereof based, at least in part, on the one or more patterns.

8. A method of claim 1 further comprising:

determining one or more information state trajectories based, at least in part, on an inference action, a conceptual clustering, an analogy creation, one or more working conditions, a fault prediction, one or more usage patterns, a workload estimation, or a combination, wherein the one or more data items are retrieved from the plurality of the partitions within a plurality of distributed storages based, at least in part, on the one or more social graphs, the one or more future states, or a combination thereof.

9. A method of claim 1, further comprising:

serializing one or more computation closures associated with the processing of the one or more social graphs, the prediction of the one or more future states, the clustering of the one or more data items, or a combination thereof; and

associating the serialization with respective ones of the one or more data items, the at least one information space, or a combination thereof.

19. A method of claim 1, wherein the at least one computation space is further based, at least in part, on computations associated with a personalization process and creation of a personal cloud.

10. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,

process one or more social graphs associated with one or more users to cause a prediction of one or more future states of the one or more social graphs, wherein processing the one or more social graphs comprises,

determining respective information trajectories defining a finite set of possible, nearest future states of the respective one or more social graphs,

extracting historical patterns based on the one or more social graphs, and

selecting the one or more future states from the set of possible future states based at least in part on the most recent historical patterns; and

cause a clustering of one or more data items distributed over a plurality of storage partitions in a cloud, associated with at least one information space and at least one computation space, based, at least in part, on the one or more social graphs, the one or more future states, or a combination thereof, wherein the at least one information space comprises data associated with a user of a social network and the at least one computation space comprises computation closures associated with computation resources of the cloud, based, at least in part, on processing and maintaining the one or more social graphs, wherein computation closures identify a respective computation procedure together with relations and communications among one or more processes including one or more of passing arguments, sharing process results, selecting results provided from computation of alternative inputs or flow of data.

11. An apparatus of claim 10, wherein the apparatus is further caused to:

process one or more attributes associated with the one or more users to determine the one or more social graphs, the one or more future states, the one or more data items, or a combination thereof.

12. An apparatus of claim 10, wherein the apparatus is further caused to:

determine at least one sequence of one or more information management processes associated with the one or more data items;

process the at least one sequence to determine one or more process states of the respective one or more information management processes;

determine one or more information state trajectories based, at least in part, on at least one sequence, the one or more process states, or a combination thereof; and

cause the prediction of the one or more future states based, at least in part, on the one or more information state trajectories.

13. An apparatus of claim 12, wherein the apparatus is further caused to:

process the one or more information state trajectories, the one or more future states, or a combination thereof to determine recyclability information associated with the one or more data items,

wherein the clustering of the one or more data items is based, at least in part, on the recyclability information.

14. An apparatus of claim 12, wherein the apparatus is further caused to:

determine at least one model state space based, at least in part, on the one or more state trajectories, the one or more process states, or a combination thereof; and

cause the prediction of the one or more future states based, at least in part, on an observation of the at least one model state space.

15. An apparatus of claim 12, wherein the one or more process states include, at least in part, one or more active states, one or more inactive states, one or more transitional states, or a combination thereof, and the one or more information state trajectories include, at least in part, one or more regular motions, one or more stochastic motions, or a combination thereof with respect to the one or more process states.

16. An apparatus of claim 10, wherein the apparatus is further caused to:

determine a history of utilization of the one or more data items, the at least one information space, or a combination thereof;

process the history to cause, at least in part, an extraction of one or more patterns; and

determine the one or more finite sets, the one or more possible future states, the one or more future states, or combination thereof based, at least in part, on the one or more patterns.

17. An apparatus of claim 10, wherein the apparatus is further caused to:

determine one or more information state trajectories based, at least in part, on an inference action, a conceptual clustering, an analogy creation, one or more working conditions, a fault prediction, one or more usage patterns, a workload estimation, or a combination,

wherein the one or more data items are retrieved from the plurality of the partitions within a plurality of distributed storages based, at least in part, on the one or more social graphs, the one or more future states, or a combination thereof.

18. An apparatus of claim 10, wherein the apparatus is further caused to:

cause, at least in part, a serialization of one or more computation closures associated with the processing of the one or more social graphs, the prediction of the one or more future states, the clustering of the one or more data items, or a combination thereof; and

cause, at least in part, an association of the serialization with respective ones of the one or more data items, the at least one information space, or a combination thereof.

20. An apparatus of claim 10, wherein the at least one computation space is further based, at least in part, on computations associated with a personalization process and creation of a personal cloud.