Association Rule Based Situation Awareness in Web-Based Environmental Monitoring Systems

  • Meng Zhang
  • Byeong Ho Kang
  • Quan Bai
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 124)


The Tasmanian ICT of CSIRO developed a Sensor Web test-bed system for the Australian water domain. This system provides an open platform to access and integrate near real time water information from distributed sensor networks. Traditional hydrological models can be adopted to analyze the data on the Sensor Web system. However, the requirements on high data quality and high level domain knowledge may greatly limit the application of these models. To overcome some these limitations, this paper proposes a data mining approach to analyze patterns and relationships among different hydrological events. This approach provides a flexible way to make use of data on the Hydrological Sensor Web.


Sensor Web data mining association rules knowledge discovery data presentation 


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  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Minig association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Internatinal Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Anthony, H., Vinny, C.: Route profiling: putting context to work. In: Proceedings of the 2004 ACM Symposium on Applied Computing (2004)Google Scholar
  3. 3.
    Arawal, R., Srikant, R.: Fast algorithms for mining association rues in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, SanFrancisco, pp. 487–499 (1994)Google Scholar
  4. 4.
    Beven, K.: Rainfall-runoff modeling: The Primer. John Wiley & Sons, Chichester (2004)Google Scholar
  5. 5.
    Ian, H.: Data Mining Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  6. 6.
    Ikuhisa, M., Michihiko, M., Tsuneo, A., Noboru, B.: Sensing web: to globally share sensory data avoiding privacy invasion. In: Proceedings of the 3rd International Universal Communication Symposium (2009)Google Scholar
  7. 7.
    Jeffery, W.S.: Data Mining: An Overview. Congress Research Service (2004)Google Scholar
  8. 8.
    Jiawei, H., Micheline, K.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher, San Francisco (2006)zbMATHGoogle Scholar
  9. 9.
    Klein, A., Lehner, W.: Representing data quality in sensor data streaming environments. Proceedings of the ACM J. Data Inform. (2009)Google Scholar
  10. 10.
    Liang, X., Liang, Y.: Applications of data mining in hydrology. In: Proceedings of the IEEE International Conference on Data Mining, pp. 617–620 (2001)Google Scholar
  11. 11.
    Mark, H., Eibe, F., Geoffrey, H., Bernhard, P., Peter, R., Ian, H.W.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  12. 12.
    Mulligan, M.: Modeling catchment hydrology, pp. 108–121. John Wiley & Sons, Chichester (2004)Google Scholar
  13. 13.
    Pittelkow, Y.E., Wilson, S.R.: Visualization of Gene Expression Data. The Berkeley Electronic Press (2009)Google Scholar
  14. 14.
    Open Geospatial Consortium, OGC Sensor Web Enablement: Overview and High Level Architecture. Technical Report OGC 07-165 (2007) Google Scholar
  15. 15.
    Liu, Q., Bai, Q., Terhorst, A.: Provenance-Aware Hydrological Sensor Web. In: The Proceedings of Hydroinformatics Conference, Tianjin, China, pp. 1307–1315 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Meng Zhang
    • 1
  • Byeong Ho Kang
    • 1
  • Quan Bai
    • 2
  1. 1.School of Computing and Information SystemsUniversity of TasmaniaHobartAustralia
  2. 2.CSIROTasmanian ICT CentreHobartAustralia

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