DISCOVERY OF VIEWSHEDS AND VANTAGE POINTS BY MINING GEO-TAGGED DATA

• US 20150199380A1
• Filed: 01/16/2014
• Published: 07/16/2015
• Est. Priority Date: 01/16/2014
• Status: Active Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Alme, Christopher, Koukoumidis, Emmanouil, Bryar, Norm, Bice, Anthony, Sundararajan, Arjun, Ali, Mohamed H., Avramova, Vanya
• Application Number:
  US14/156,651
• Publication Number:
  US 20150199380A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

Architecture that obtains and utilizes collections of geographically-tagged data to discover optimal vantage points for viewsheds of entities of interest such as physical entities and conceptual entities such as landmarks, sunset, skyline, etc. The disclosed architecture discloses the utilization of at least geo-tagged image data to discover relationships between a combination of concrete entities and/or abstract concepts, and techniques for surfacing such relationships to users. The data can be crowd-sourced geo-tagged image data that are mined from social content and which can be observed or experienced from a certain location/area.

Claims

1. A system, comprising:

an analysis component configured to analyze geographically-tagged data associated with an entity of interest and to discover relationships between location-based entities and the entity of interest, the entity of interest is an object or an abstract concept;

a view generation component configured to generate vantage-point information from which to view the entity of interest based on the discovered relationships; and

at least one microprocessor that executes computer-executable instructions in a memory associated with the analysis component and the view generation component.

2. The system of claim 1, wherein the geographically-tagged data is crowd-sourced image data.

3. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with popularity data and source credibility data.

4. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with emotional data derived from an association with the geographically-tagged data.

5. The system of claim 1, further comprising a recommendation component configured to recommend a new entity of interest based on the relationships and relationship conditions, as part of exploring the entity of interest.

6. The system of claim 1, further comprising a conditions component configured to detect at least one of spatial or temporal conditions under which the relationships are active.

7. The system of claim 1, further comprising a graphing component configured to generate graphs of entities of interest and relationships between the entities of interest to discover new entities.

8. The system of claim 1, wherein the geographically-tagged data is obtained from a social network and the analysis component employs machine-learning techniques to identify and de-noise the geographically tagged data.

9. A method, comprising acts of:

accessing geographically-tagged data associated with an entity of interest that is an object or an abstract concept;

analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest; and

generating vantage-point information from which to view the entity of interest based on the discovered relationships.

10. The method of claim 9, further comprising deriving conditions under which the relationships are active.

11. The method of claim 9, further comprising augmenting the vantage-point information with popularity data and source credibility data.

12. The method of claim 9, further comprising augmenting the relationships with emotional data associated with the geographically-tagged data.

13. The method of claim 9, further comprising constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest.

14. The method of claim 9, further comprising recommending new entities of interest based on the relationships and relationship conditions.

15. The method of claim 9, further comprising recommending related entities of interest while exploring the entity of interest.

16. A computer-readable storage medium comprising computer-executable instructions that when executed by a microprocessor, cause the microprocessor to perform acts of:

accessing geographically-tagged data associated with an entity of interest, the entity of interest a physical object or an abstract concept;

analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest;

generating vantage-point information from which to view the entity of interest based on the discovered relationships; and

constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest.

17. The computer-readable storage medium of claim 16, further comprising augmenting the vantage-point information with popularity data and source credibility data, and augmenting the relationships with emotional data associated with the geographically-tagged data.

18. The computer-readable storage medium of claim 16, further comprising recommending new entities of interest based on the relationships and relationship conditions.

19. The computer-readable storage medium of claim 16, further comprising recommending related entities of interest while exploring the entity of interest.

20. The computer-readable storage medium of claim 16, further comprising deriving conditions under which the relationships are active.