Discover and visualize association rules from sensor observations on the web

Authors:
Meng Zhang;Byeong Ho Kang;Quan Bai
Affiliations:
School of Computing and Information Systems, University of Tasmania, Hobart, Australia;School of Computing and Information Systems, University of Tasmania, Hobart, Australia;School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

Nowadays, Web-based applications has became a common practice in environment monitoring. These applications provide open platforms for users to discover access and integrate near real-time sensor data which is collected from distributed sensors and sensor networks. To make use of the shared sensor data on the Web, conceptual models in a particular domain are normally adopted. However, most conceptual models require high quality data and high level domain knowledge. Such limitations greatly limit the application of these models. To overcome some of these limitations, this paper proposes a data-mining approach to analyze patterns and relationships among different sensor data sets. This approach provides a flexible way for users to understand hidden relationships in shared sensor data, and can help them to make use Web-based sensor systems better.