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On retrieving patterns in environmental sensor data

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Abstract

As many sensor networks are currently being deployed for environmental monitoring, there is a growing need to develop systems and applications for managing, processing and retrieving massive amounts of data generated from those networks. In this research, a query answering system with pattern mining techniques is investigated specifically for marine sensor data. We consider three applications of pattern mining: similar pattern search, predictive query and query by clustering. In pattern mining for query answering, we adopt the dynamic time warping (DTW) method for similarity measurement. We also propose the use of a query relaxation approach that recommends users change parameters of a given query to get an answer. Finally, we show implementation results of pattern query answering in a marine sensor network deployed in the South East of Tasmania, Australia. Pattern query answering system benefits in accessing and discovering knowledge from sensor data for decision making purposes.

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Acknowledgements

The Tasmanian ICT Centre is jointly funded by the Australian Government through the Intelligent Island Program and CSIRO. The Intelligent Island Program is administered by the Tasmanian Department of Economic Development, Tourism and the Arts. This research was conducted as part of the CSIRO Wealth from Oceans National Research Flagship and the Sensors and Sensor Networks Transformational Capability Platform(SSN-TCP). We thank Aidan O’Mara for providing improved prediction using clustering.

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Authors and Affiliations

  1. Tasmanian ICT Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Castray Esplanade, Hobart, 7001, Australia

    Md. Sumon Shahriar, Paulo de Souza & Greg Timms

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  1. Md. Sumon Shahriar
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  2. Paulo de Souza
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  3. Greg Timms
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Correspondence to Md. Sumon Shahriar.

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Communicated by: H. A. Babaie

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Shahriar, M.S., Souza, P.d. & Timms, G. On retrieving patterns in environmental sensor data. Earth Sci Inform 5, 43–59 (2012). https://doi.org/10.1007/s12145-012-0095-x

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