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International Conference on Extending Database Technology

EDBT 2006: Current Trends in Database Technology – EDBT 2006 pp 1–11Cite as

Phenomenon-Aware Sensor Database Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4254))

Abstract

Recent advances in large-scale sensor-network technologies enable the deployment of a huge number of sensors in the surrounding environment. Sensors do not live in isolation. Instead, close-by sensors experience similar environmental conditions. Hence, close-by sensors may indulge in a correlated behavior and generate a “phenomenon”. A phenomenon is characterized by a group of sensors that show “similar” behavior over a period of time. Examples of detectable phenomena include the propagation over time of a pollution cloud or an oil spill region. In this research, we propose a framework to detect and track various forms of phenomena in a sensor field. This framework empowers sensor database systems with phenomenon-awareness capabilities. Phenomenon-aware sensor database systems use high-level knowledge about phenomena in the sensor field to control the acquisition of sensor data and to optimize subsequent user queries. As a vehicle for our research, we build the Nile-PDT system, a framework for Phenomenon Detection and Tracking inside Nile, a prototype data stream management system developed at Purdue University.

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References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB (1994)

    Google Scholar 

  2. Ali, M.H., Aref, W.G., Bose, R., Elmagarmid, A.K., Helal, A., Kamel, I., Mokbel, M.F.: Nile-pdt: A phenomena detection and tracking framework for data stream management systems. In: Proc. of VLDB (2005)

    Google Scholar 

  3. Ali, M.H., Aref, W.G., Kamel, I.: Multi-way joins for sensor-network databases. Technical Report CSD-05-21, Department of Computer Science, Purdue University (2005)

    Google Scholar 

  4. Ali, M.H., Aref, W.G., Nita-Rotaru, C.: Spass: Scalable and energy-efficient data acquisition in sensor databases. In: Proc. of the International ACM Workshop on Data Engineering for Wireless and Mobile Access (MobiDE) (2005)

    Google Scholar 

  5. Ali, M.H., Mokbel, M.F., Aref, W.G., Kamel, I.: Detection and tracking of discrete phenomena in sensor-network databases. In: Proc. of SSDBM (2005)

    Google Scholar 

  6. Babcoc, B., Datar, M., Motwani, R.: Sampling from a moving window over streaming data. In: Proc. of the Annual ACM-SIAM Symp. on Discrete Algorithms (2002)

    Google Scholar 

  7. Bonnet, P., Gehrke, J.E., Seshadri, P.: Towards sensor database systems. In: Proc. of MDM (2001)

    Google Scholar 

  8. Cerpa, A., Estrin, D.: Ascent: Adaptive self-configuring sensor networks topologies. In: Proc. of INFOCOM (2002)

    Google Scholar 

  9. Considine, J., Li, F., Kollios, G., Byers, J.W.: Approximate aggregation techniques for sensor databases. In: Proc. of ICDE (2004)

    Google Scholar 

  10. Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In: Proc. of VLDB (2004)

    Google Scholar 

  11. Golab, L., Ozsu, M.T.: Processing sliding window multi-joins in continuous queries over data streams. In: Proc. of VLDB (2003)

    Google Scholar 

  12. Hammad, M.A., Aref, W.G., Elmagarmid, A.K.: Stream window join: Tracking moving objects in sensor-network databases. In: Proc. of SSDBM (2003)

    Google Scholar 

  13. Hammad, M.A., Franklin, M., Aref, W.G., Elmagarmid, A.K.: Scheduling for shared window joins over data streams. In: Proc. of VLDB (2003)

    Google Scholar 

  14. Hammad, M.A., Mokbel, M.F., Ali, M.H., Aref, W.G., Catlin, A.C., Elmagarmid, A.K., Eltabakh, M., Elfeky, M.G., Ghanem, T., Gwadera, R., Ilyas, I.F., Marzouk, M., Xiong, X.: Nile: A query processing engine for data streams. In: Proc. of ICDE (2004)

    Google Scholar 

  15. Hellerstein, J.M., Hong, W., Madden, S., Stanek, K.: Beyond average: Toward sophisticated sensing with queries. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 63–79. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  16. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proc. of MOBICOM (2000)

    Google Scholar 

  17. Kang, J., Naughton, J.F., Viglas, S.D.: Evaluating window joins over unbounded streams. In: Proc. of ICDE (2003)

    Google Scholar 

  18. Kulik, J., Heinzelman, W.R., Balakrishnan, H.: Negotiation-based protocols for disseminating information in wireless sensor networks. ACM Wireless Networks 8(2-3), 169–185 (2002)

    Article  MATH  Google Scholar 

  19. Madden, S., Franklin, M.: Fjording the stream: An architecture for queries over streaming sensor data. In: Proc. of ICDE (2002)

    Google Scholar 

  20. Madden, S., Franklin, M., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: Proc. of SIGMOD (2003)

    Google Scholar 

  21. Mokbel, M., Lu, M., Aref, W.: Hash-merge join: A non-blocking join algorithm for producing fast and early join results. In: Proc. of ICDE (2004)

    Google Scholar 

  22. Nowak, R., Mitra, U.: Boundary estimation in sensor networks: Theory and methods. In: Proc. of IPSN (2003)

    Google Scholar 

  23. Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Proc. of EDBT (1996)

    Google Scholar 

  24. Srinivasan, S., Latchman, H., Shea, J., Wong, T., McNair, J.: Airborne traffic surveillance systems: video surveillance of highway traffic. In: The 2nd ACM international workshop on Video surveillance & sensor networks (2004)

    Google Scholar 

  25. Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A., Estrin, D.: Habitat monitoring with sensor networks. Communications of ACM 47(6), 34–40 (2004)

    Article  Google Scholar 

  26. Urhan, T., Franklin, M.: Xjoin: A reactively-scheduled pipelined join operator. IEEE Data Eng. Bull. 23(2), 27–33 (2000)

    Google Scholar 

  27. Wilschut, A.N., Apers, E.M.G.: Pipelining in query execution. In: Proc. of the International Conference on Databases, Parallel Architectures and their Applications (1991)

    Google Scholar 

  28. Xu, Y., Winter, J., Lee, W.-C.: Prediction-based strategies for energy saving in object tracking sensor networks. In: Proc. of MDM (2004)

    Google Scholar 

  29. Yao, Y., Gehrke, J.: Query processing in sensor networks. In: Proc. of CIDR (2003)

    Google Scholar 

  30. Zhang, W., Cao, G.: Optimizing tree reconfiguration for mobile target tracking in sensor networks. In: Proc. of INFOCOM (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Purdue University,  

    M. H. Ali

Authors
  1. M. H. Ali
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Editor information

Editors and Affiliations

  1. Technische Universität München, Germany

    Torsten Grust

  2. International University in Germany, Germany

    Hagen Höpfner

  3. University of the Basque Country,  

    Arantza Illarramendi

  4. University of Bayreuth, Bayreuth, Germany

    Stefan Jablonski

  5. Università di Milano, Italy

    Marco Mesiti

  6. University of Erlangen-Nurmberg, Germany

    Sascha Müller

  7. Institut für Informatik, Ludwig-Maximilians-Universität, Oettingenstr. 67, 80538, München, Germany

    Paula-Lavinia Patranjan

  8. Dept. of Computer Science and Automation, TU Ilmenau

    Kai-Uwe Sattler

  9. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

  10. Université de Mons-Hainaut, Mons, Belgium

    Jef Wijsen

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© 2006 Springer-Verlag Berlin Heidelberg

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Ali, M.H. (2006). Phenomenon-Aware Sensor Database Systems. In: Grust, T., et al. Current Trends in Database Technology – EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 4254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11896548_1

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  • DOI: https://doi.org/10.1007/11896548_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46788-5

  • Online ISBN: 978-3-540-46790-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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