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Assessing the Completeness of Sensor Data

  • Jit Biswas
  • Felix Naumann
  • Qiang Qiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)

Abstract

In this paper we present a quality model highlighting the completeness of sensor data with respect to its application. The model allows consistent handling of information loss as data propagates through a sensor network. The tradeoffs between various factors that influence completeness are quantified thereby allowing an integrated view of completeness at various levels in a system. The paper is presented in the context of the fast emerging field of smart spaces. All concepts in the paper have a foundation in real-life problems arising in this context. Preliminary implementation results are presented to illustrate the value of the completeness based approach versus one that does not use completeness.

Keywords

Sensor Network Sensor Data Query Processing Smart Home Ultrasonic Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wang, X., Dong, J.S., Zhang, D., Chin, C.Y., Hettiarachchi, S.R.: Semantic space: An infrastructure for smart spaces. IEEE Pervasive Computing Magazine, 32–39 (2004)Google Scholar
  2. 2.
    Bardram, J.E.: Applications of context-aware computing in hospital work - examples and design principles. In: Proceedings of the 2004 ACM Symposium on Applied Computing (2004)Google Scholar
  3. 3.
    Tolstikov, A., Biswas, J., Chen-Khong, T.: Data loss regulation to ensure information quality in sensor networks. In: Proceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference, pp. 133–138 (2005)Google Scholar
  4. 4.
    Biswas, J., Das, S., Qiu, Q., Chava, V.S., Thang, P.: Quality aware elderly people monitoring using ultrasonic sensors. In: Proceedings of the International Conference On Smart Homes and Health Telematics (ICOST), pp. 107–115 (2005)Google Scholar
  5. 5.
    Biswas, J., Yap, P., Foo, V., Qiu, Q., Aung, A.P.W., Thang, P.V., Guopei, Q.: Use of pervasive monitoring technology as compared to direct observational methods using the soapd scale in the measurement of agitation in patients with dementia. In: Research Collaboration between Institute for Infocomm Research (I2R) and Alexandra Hospital, Singapore (2005)Google Scholar
  6. 6.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: Proceedings of the International Conference on Mobile Computing and Networking (MobiCom) (2000)Google Scholar
  7. 7.
    Lazaridis, I., Mehrotra, S.: Capturing sensor-generated time series with quality guarantees. In: Proceedings of the International Conference on Data Engineering (ICDE) (2003)Google Scholar
  8. 8.
    Naumann, F., Freytag, J.C., Leser, U.: Completeness of integrated information sources. Information Systems 29(7), 583–615 (2004)CrossRefGoogle Scholar
  9. 9.
    Leser, U., Naumann, F.: Query planning with information quality bounds. In: Proceedings of the International Conference on Flexible Query Answering Systems (FQAS), Warsaw, Poland. Advances in Soft Computing, Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Motro, A., Rakov, I.: Estimating the quality of databases. In: Proceedings of the International Conference on Flexible Query Answering Systems (FQAS), pp. 298–307. Springer, Roskilde, Denmark (1998)CrossRefGoogle Scholar
  11. 11.
    Motro, A.: Completeness information and its application to query processing. In: Proceedings of the International Conference on Very Large Databases (VLDB), Kyoto, pp. 170–178 (1986)Google Scholar
  12. 12.
    Florescu, D., Koller, D., Levy, A.: Using probabilistic information in data integration. In: Proceedings of the International Conference on Very Large Databases (VLDB), Athens, Greece, pp. 216–225 (1997)Google Scholar
  13. 13.
    Terry, D., Goldberg, D., Nichols, D., Oki, B.: Continuous queries over append-only databases. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD) (1992)Google Scholar
  14. 14.
    Babu, S., Widom, J.: Continuous queries over data streams. In: SIGMOD Record, pp. 109–120 (2001)Google Scholar
  15. 15.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of the Symposium on Principles of Database Systems (PODS) (2002)Google Scholar
  16. 16.
    Motwani, R., Widom, J., Arasu, A., Babcock, B., Babu, S., Datar, M., Manku, G., Olston, C., Rosenstein, J., Varma, R.: Query processing, resource management, and approximation in a data stream management system. In: Proceedings of the Conference on Innovative Data Systems Research (CIDR), pp. 245–256 (2003)Google Scholar
  17. 17.
    Abadi, D.J., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: Aurora: A new model and architecture for data stream management. VLDB Journal 12(2), 120–139 (2003)CrossRefGoogle Scholar
  18. 18.
    Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tinydb: An acquisitional query processing system for sensor networks. ACM Transactions on Database Systems (TODS) 30(1), 122–173 (2005)CrossRefGoogle Scholar
  19. 19.
    Demers, A., Gehrke, J., Rajaraman, R., Trigoni, N., Yao, Y.: The Cougar project: A work-in-progress report. In: SIGMOD Record, vol. 32 (2003)Google Scholar
  20. 20.
    Yao, Y., Gehrke, J.: Query processing for sensor networks. In: Proceedings of the Conference on Innovative Data Systems Research (CIDR) (2003)Google Scholar
  21. 21.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. Technical Report TR2000-1819, Cornell University (2000)Google Scholar
  22. 22.
    Gedik, B., Liu, L.: Mobieyes: Distributed processing of continuously moving queries on moving objects in a mobile system. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Madden, S., Shah, M.A., Hellerstein, J.M., Raman, V.: Continuously adaptive continuous queries over streams. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jit Biswas
    • 1
  • Felix Naumann
    • 2
  • Qiang Qiu
    • 1
  1. 1.Institute for Infocomm Research (I2R)Singapore
  2. 2.Humboldt-Universität zu BerlinGermany

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