Apparatus and Method Of Map Engine Clustering Automation

• US 20080281869A1
• Filed: 05/07/2008
• Published: 11/13/2008
• Est. Priority Date: 05/07/2007
• Status: Abandoned Application

• Current Assignee: Enowhiring Incorporated
• Original Assignee: Enowhiring Incorporated
• Inventors: Liu, Li, Lai, Yin Chung
• Application Number:
  US12/116,537
• Publication Number:
  US 20080281869A1
• Time in Patent Office: Days
• US Class Current: 707/104.1
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

A system for clustering map information includes a map engine clustering automation (MECA) module communicating with a map engine to receive at least map content data from the map engine. In one embodiment, the MECA module clusters the at least map content data automatically to provide at least clustered map content data that is clustered on the basis of proximity. The system also includes a client communicating with the MECA module to receive the at least clustered map content data and display the at least clustered map content data. A method of map engine clustering automation is also included. In one embodiment, the method includes filtering a list of postings resulting in a first list of remaining postings. The first list is clustered so that postings that are substantially proximate are clustered resulting in clustered map points corresponding to postings which were clustered.

Claims

1. An apparatus of map engine clustering automation, the apparatus comprising:

a map engine clustering automation (MECA) module communicating with the map engine to receive at least map content data from the map engine, the MECA module clustering the at least map content data automatically to provide at least clustered map content data that is clustered on the basis of proximity; and

a client communicating with the MECA module to receive the at least clustered map content data, the client displaying the at least clustered map content data.

2. The apparatus as recited in claim 1, wherein the client is a web browser client.

3. The apparatus as recited in claim 1, wherein the at least clustered map content data includes information related to a posting.

4. The apparatus as recited in claim 3, wherein the posting is selected from the group consisting of a job posting and a real-estate posting.

5. The apparatus as recited in claim 1, wherein the map engine is an online mapping service.

6. The apparatus as recited in claim 5, wherein the online mapping service is selected from the group consisting of Google Maps, Microsoft Virtual Earth, and Yahoo! Maps.

7. The apparatus as recited in claim 1, wherein the map engine is selected from the group consisting of Microsoft MapPoint, Google Earth, and Microsoft Streets and Maps.

8. The apparatus as recited in claim 1, further comprising a mashup server communicating with the MECA module to provide content data to the MECA module.

9. The apparatus as recited in claim 8, wherein the MECA module is provided at the mashup server.

10. The apparatus as recited in claim 1, further comprising a mapping server communicating with the MECA module to provide the map data to the MECA module via the mapping engine.

11. The apparatus as recited in claim 10, wherein the MECA module is provided at the mapping server.

12. The apparatus as recited in claim 1, wherein the at least clustered map content data includes filtered map content data.

13. The apparatus as recited in claim 1, wherein the at least clustered map content data includes unfiltered map content data.

14. A method of map engine clustering automation, the method comprising the steps of:

receiving map data from the map engine;

retrieving a list of postings for processing in relation to the map data;

filtering the list of postings based on criteria so that postings that match the criteria are filtered according to a filtering operation resulting in filtered map points corresponding to postings which were filtered and a first list of remaining postings which were not filtered;

clustering the first list of remaining postings so that postings that are substantially proximate are clustered according to a clustering operation resulting in clustered map points corresponding to postings which were clustered and a second list of remaining postings which were not clustered; and

providing a list of points and related posting information destined to be displayed on a map.

15. The method as recited in claim 14, wherein the clustering step includes the act of comparing two points on the map which are related to two postings.

16. The method as recited in claim 15, wherein the clustering step includes the act of computing a distance between the two points.

17. The method as recited in claim 16, wherein the clustering step includes the act of comparing the distance to a threshold value.

18. The method as recited in claim 16, wherein the distance is measured along a line.

19. The method as recited in claim 18, wherein the line extends from one of the two points to the other of the two points.

20. The method as recited in claim 18, wherein the line is a latitude projection of a line that extends from one of the two points to the other of the two points.

21. The method as recited in claim 18, wherein the line is a longitude projection of a line that extends from one of the two points to the other of the two points.

22. The method as recited in claim 17, wherein the threshold value depends on a current zoom level for a map.

23. The method as recited in claim 14, wherein the clustering operation includes the act of providing a cluster point which is related to at least two postings.

24. The method as recited in claim 23, wherein the cluster point is determined as a function of the location of the at least two postings.

25. The method as recited in claim 24, wherein the cluster point is determined as an average of the locations of the at least two postings.

26. The method as recited in claim 24, wherein the cluster point is determined by selecting a location from the locations of the at least two postings.

27. The method as recited in claim 14, wherein the list of points in the returning step includes the clustered map points and wherein the related posting information includes information related to the postings that were clustered.

28. The method as recited in claim 14, wherein the list of points in the returning step includes the filtered map points and wherein the related posting information includes information related to the postings that were filtered.

29. The method as recited in claim 14, wherein the list of points in the returning step includes map points corresponding to postings which were neither clustered nor filtered and wherein the related posting information includes information related to the postings that were neither clustered nor filtered.

30. The method as recited in claim 14, wherein the list of points in the returning step includes map points corresponding to postings which were either clustered, filtered or neither clustered nor filtered and wherein the related posting information includes information related to the postings that were either clustered, filtered or neither clustered nor filtered.

31. The method as recited in claim 14, wherein the criteria at least includes the criterion that the location of the postings is outside a bounding region, and wherein the filtering operation is to truncate the postings from the list of postings.

32. The method as recited in claim 31, wherein the bounding region is a visible area of a map.

33. The method as recited in claim 31, wherein the bounding region is defined by where a visitor moves to on a visible area of a map.

34. The method as recited in claim 14, wherein the filtering operation further comprising the acts of:

selecting a candidate posting from the list of postings;

identifying matching postings using the criteria and the candidate posting;

creating a point to represent the matching postings identified; and

not listing the identified postings and the candidate posting in the list of remaining postings.

35. The method as recited in claim 34, wherein the filtering operation further comprises the act of determining whether any other candidate posting has not yet been considered for filtering, and if so determined repeating the selecting step with each of the other candidate posting until all candidate postings have been considered for filtering.

36. The method as recited in claim 35, further comprising the step of performing other filtering using another criteria and another filtering operation.

37. The method as recited in claim 14, wherein postings include a mall identifier, and wherein the criteria comprises a criterion of postings that have the same mall identifier.

38. The method as recited in claim 14, wherein postings include a region identifier, and wherein the criteria comprises a criterion of postings that have the same region identifier.

39. The method as recited in claim 14, wherein postings include a company identifier, and wherein the criteria comprises a criterion of postings that have the same company identifier.

40. The method as recited in claim 14, wherein postings include location, and wherein the criteria comprises a criterion of postings that have substantially the same location.

41. The method as recited in claim 40, wherein postings further include a company identifier, and wherein the criteria further comprises a criterion of postings that have the same company identifier.

42. The method as recited in claim 14, wherein postings include location, and wherein the criteria comprises the criterion of postings that are within a radius of the location of the candidate posting at a current zoom level.

43. The method as recited in claim 14, wherein the postings are selected from the group consisting of a job posting and a real-estate posting.

44. The method as recited in claim 14, wherein the map engine is an online mapping service.