Enabling Query Technologies for the Semantic Sensor Web

Author:

Calbimonte Jean-Paul1,Jeung Hoyoung2,Corcho Oscar1,Aberer Karl3

Affiliation:

1. Universidad Politécnica de Madrid, Spain

2. SAP Research, Australia

3. Ecole Polytechnique Fédérale de Lausanne, Switzerland

Abstract

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

Publisher

IGI Global

Subject

Computer Networks and Communications,Information Systems

Reference40 articles.

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2. W3C. (2004). Resource description framework (RDF). Retrieved from http://www.w3.org/RDF/

3. Abadi, D. J., Ahmad, Y., Balazinska, M., Cetintemel, U., Cherniack, M., & Hwang, J.-H. …Zdonik, S. (2005). The design of the borealis stream processing engine. In Proceedings of the 2nd Conference on Innovative Database Research (pp. 277-289).

4. Aberer, K., Hauswirth, M., & Salehi, A. (2006). A middleware for fast and flexible sensor network deployment. In Proceedings of the 32nd International Conference on Very Large Databases (pp. 1199-1202).

5. Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., & Ito, K. …Widom, J. (2007). STREAM: The Stanford data stream management system. In M. Garofalakis, J. Gehrke, & R. Rastogi (Eds.), Data stream management. New York, NY: Springer.

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