Method and apparatus for fusing context data

• US 20040111397A1
• Filed: 12/06/2002
• Published: 06/10/2004
• Est. Priority Date: 12/06/2002
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: International Business Machines Corporation
• Inventors: Chen, Ying, Lei, Hui, Myllymaki, Jussi P.
• Granted Patent: US 7,392,247 B2
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A system and method for fusing context data for use by context aware applications. The method includes the steps of receiving context data from a plurality of information sources; computing a quality measure for each input context value; organizing context values into one or more clusters, and assigning a single context value and a single quality measure to each cluster; and, selecting one or more clusters according to one or more criteria and aggregating the context values and quality measures of selected clusters to generate a single context value and quality single measure. The single context value and single quality measure are usable by a context aware application to avoid conflict and ambiguity among different information sources providing the context data.

Claims

1. A method for fusing context data comprising the steps of:

(a) receiving context data from a plurality of information sources;

(b) computing a quality measure for each input context value;

(c) organizing context values into one or more clusters, and assigning a single context value and a single quality measure to each cluster; and

,

d) selecting one or more clusters according to one or more criteria and aggregating the context values and quality measures of selected clusters to generate a single context value and single quality measure,

wherein said single context value and single quality measure are usable by a context aware application to avoid conflict and ambiguity among different information sources providing said context data.

2. The method of claim 1, wherein prior to said computing step b), the further step of receiving attributes of the context data from sources, said sources including a sensor device.

3. The method of claim 1, further comprising the step of guiding clustering, selecting and aggregating operations used in generating said single context value, said guiding performed according to a respective clustering policy, selection policy, and aggregation policy.

4. The method of claim 3, further including the step of maintaining default clustering, selection and aggregation policies, said clustering, selecting and aggregating operations being automatically selected.

5. The method of claim 3, further including the step of enabling a user to specify said clustering, selection and aggregation policies used for guiding said clustering, selecting and aggregating operations.

6. The method of claim 3, wherein a clustering policy implements steps of:

assigning context values into groups; and

for each group, combining context values of that group into said single context value, and combining quality measures of that group into said single quality measure.

7. The method of claim 6, wherein a clustering policy step of combining context values of a cluster into said single context value includes one or more steps selected from:

determining a context value having best quality measure, computing an average context value, and computing an average context value and wherein the average context value is weighted by a quality measure.

8. The method of claim 6, wherein said context values represent shape values, the step of combining context values of a cluster into said single context value includes determining an intersection of said shape values.

9. The method of claim 6, wherein said context values represent shapes values, said step of combining context values of a cluster into said single context value includes step of computing the center of gravity of said shapes values.

10. The method of claim 6, wherein the step of combining quality measures of a cluster into a single quality measure includes one or more steps of:

determining a maximum quality measure value, determining a minimum quality measure value, determining a quality measure associated with a context value representative of the cluster; and

computing an average quality measure value.

11. The method of claim 3, wherein a selection policy implements a step of selecting according to one or more criteria selected from the group comprising:

cluster size, cluster quality, and cluster tightness.

12. The method of claim 3, wherein a selection policy implements a step of selecting according to one or more of:

the desired number of selections; and

a minimum threshold quality measure.

13. The method of claim 2, wherein said step b) of computing a quality measure for each input context value includes one or more of:

accounting for attributes associated with the context data, or attributes associated with a sensor device or source providing said context data.

14. The method of claim 13, wherein said attributes associated with context data include one or more of:

a timestamp associated with context value, said timestamp indicating when a context value data is sensed or created, when a context value is last modified, and when said context value expires; and

, a sensor-asserted confidence in the context data.

15. The method of claim 13, wherein attributes associated with a sensor device include a user-perceived confidence in the sensor device or source providing the context data.

16. The method of claim 3, wherein said context values are point values, an aggregation policy for guiding aggregating of context values and quality measures of selected clusters to generate a single context value and quality single measure including the step computing an average context value.

17. The method of claim 16, wherein said computed average context value is weighted according to a quality measure.

18. The system for fusing context data obtained from a plurality of sources, said system comprising:

means for receiving said context data from one or more information sources;

means for computing a quality measure for each input context value;

means for organizing context values into one or more clusters, each cluster having a single context value and a single quality measure associated therewith; and

,

means for selecting one or more clusters according to one or more criteria and aggregating the context values and quality measures of selected clusters to generate a single context value and quality single measure;

wherein said single context value and single quality measure are usable by a context aware application to avoid conflict and ambiguity among different information sources providing said context data.

19. The system of claim 18, implementing a push model whereby updated context data is automatically received from said sources.

20. The system of claim 18, whereby said context aware application implements means for requesting updated context data from said sources.

21. The system of claim 18, further comprising means for guiding clustering, selecting and aggregating operations used in generating said single context value, said guiding performed according to a respective clustering policy, selection policy, and aggregation policy.

22. The system of claim 21, further including means for maintaining default clustering, selection and aggregation policies, said clustering, selecting and aggregating operations being automatically selected.

23. The system of claim 21, further including means for enabling a user to specify said clustering, selection and aggregation policies used for guiding said clustering, selecting and aggregating operations.

24. A computer program device readable by a machine, tangibly embodying a program of instructions executable by a machine to perform method steps for fusing context data, said method steps comprising:

(a) receiving context data from a plurality of information sources;

(b) computing a quality measure for each input context value;

(c) organizing context values into one or more clusters, and assigning a single context value and a single quality measure to each cluster; and

,

d) selecting one or more clusters according to one or more criteria and aggregating the context values and quality measures of selected clusters to generate a single context value and quality single measure,

wherein said single context value and single quality measure are usable by a context aware application to avoid conflict and ambiguity among different information sources providing said context data.