Publication Type

Conference Proceeding Article

Publication Date

3-2008

Abstract

We consider an environment of numerous moving objects, equipped with location-sensing devices and capable of communicating with a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. Motion paths approximate portions of objects' movement within a tolerance margin that depends on the uncertainty inherent in positional measurements. Discovery of hot motion paths is important to applications requiring classification/profiling based on monitored movement patterns, such as targeted advertising, resource allocation, etc. To achieve this goal, we delegate part of the path extraction process to objects, by assigning to them adaptive lightweight filters that dynamically suppress unnecessary location updates and, thus, help reducing the communication overhead. We demonstrate the benefits of our methods and their efficiency through extensive experiments on synthetic data sets.

Keywords

Approximation theory, Data structures, Problem solving, Search engines, Uncertainty analysis

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Advances in Database Technology EDBT 2008: 11th International Conference on Extending Database Technology, Nantes, France, March 25 - 29, 2008: Proceedings

First Page

392

Last Page

403

ISBN

9781595939265

Identifier

10.1145/1353343.1353392

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/1353343.1353392

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