CLUSTERING CROWD-SOURCED DATA TO IDENTIFY EVENT BEACONS

• US 20120185458A1
• Filed: 01/18/2011
• Published: 07/19/2012
• Est. Priority Date: 01/18/2011
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Liu, Weili, Bandhakavi, Sindhura, Lin, Jyh-Han
• Application Number:
  US13/008,033
• Publication Number:
  US 20120185458A1
• Time in Patent Office: Days
• US Class Current: 707/709
• CPC Class Codes: G06F 16/29 Geographical information databases

Abstract

Embodiments for identifying event beacons are provided. Position observations for a beacon are grouped into a plurality of clusters based at least on spatial distance. A location of each cluster is compared to event locations corresponding to events. Based on the comparison, the beacon is associated with the event, and the location of the beacon is set to the location of the event. In some embodiments, location requests are analyzed to identify event beacons, and the event information for the event beacons is used to identify event locations in response to the location requests.

Claims

1. A system for associating a beacon with an event, said system comprising:

a memory area associated with a computing device, said memory area storing a plurality of position observations for a beacon, said memory area further storing event information including event locations and events corresponding thereto, said memory area further storing a pre-defined threshold value; and

a processor programmed to;

identify a plurality of clusters of the position observations for the beacon, said clusters being defined by spatial distance, each of the plurality of clusters having a location associated therewith;

compare the location of each of the clusters with the event locations stored in the memory area to identify a matching event;

compare a quantity of the matching events to a pre-defined threshold value; and

based on the comparison of the quantity of the matching events to the pre-defined threshold value, associate the beacon with the matching event.

2. The system of claim 1, wherein the event information stored in the memory area describes one or more of the following:

past events, present events, and future events.

3. The system of claim 1, wherein the processor is further programmed to assign the event location of the matching event to the beacon.

4. The system of claim 1, wherein the plurality of position observations comprise crowd-sourced records each identifying one or more beacons observed by one of a plurality of mobile computing devices.

5. The system of claim 1, wherein the processor is further programmed to obtain the event information via a web crawler.

6. The system of claim 1, further comprising means for identifying the beacon as an event beacon, and means for associating the beacon with the event.

7. The system of claim 1, further comprising means for identifying the plurality of clusters of the position observations for the beacon.

8. A method comprising:

identifying, by a computing device, a plurality of clusters of position observations for a beacon, said clusters being defined by spatial distance, each of the plurality of clusters having a location associated therewith;

comparing the location of each of the plurality of clusters with event locations, each of the event locations corresponding to at least one event; and

based on the comparison, associating the beacon with the event.

9. The method of claim 8, further comprising defining the beacon as an event beacon.

10. The method of claim 8, wherein each of the plurality of clusters has a time associated therewith, and wherein comparing the location of each of the plurality of clusters with event locations comprises comparing the location and time of each of the plurality of clusters with the event locations and event times associated therewith.

11. The method of claim 8, further comprising receiving the position observations from a plurality of mobile computing devices.

12. The method of claim 8, wherein identifying the plurality of clusters comprises grouping the position observations for the beacon into a plurality of clusters.

13. The method of claim 8, further comprising calculating the location of each of the plurality of clusters.

14. The method of claim 8, further comprising receiving the position observations from a mobile computing device, wherein the position observations include a location of the mobile computing device and a set of beacons observed by the mobile computing device.

15. The method of claim 8, further comprising:

accessing one or more future event locations associated with the event;

predicting a future location for the beacon based on the accessed future event locations.

16. The method of claim 8, wherein identifying the plurality of clusters comprises performing a k-means clustering algorithm.

17. One or more computer-readable media having computer-executable components, said components comprising:

a memory component that when executed by at least one processor causes the at least one processor to access event locations and corresponding events;

a communications interface component that when executed by at least one processor causes the at least one processor to receive a location request from a computing device, said location request including a set of beacons observed by the computing device, wherein the set of beacons includes an event beacon and a stationary beacon of known location; and

a conference component that when executed by at least one processor causes the at least one processor to compare the known location with the event locations accessed by the memory component to identify a matching event location, wherein the communications interface component sends the matching event location to the computing device as the location of the computing device.

18. The computer-readable media of claim 17, wherein the memory component, the communications interface component, and the conference component are associated with a cloud-based service.

19. The computer-readable media of claim 17, wherein the memory component accesses event locations and corresponding events by requesting the event locations and corresponding events from a cloud-based service.

20. The computer-readable media of claim 17, wherein the conference component filters the event locations based on a current time, and identifies the matching event location by comparing the known location with the filtered event locations.