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Discover and visualize association rules from sensor observations on the web


Zhang, M and Kang, BH and Bai, Q, Discover and visualize association rules from sensor observations on the web, Journal of Supercomputing, 65, (1) pp. 4-15. ISSN 0920-8542 (2013) [Refereed Article]

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Copyright 2011 Springer Science+Business Media, LLC

DOI: doi:10.1007/s11227-011-0697-y


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.

Item Details

Item Type:Refereed Article
Keywords:sensors and sensor networks, web-based environmental monitoring, data mining, association rules, knowledge discovery, data presentation
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Zhang, M (Mr Meng Zhang)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:89003
Year Published:2013
Web of Science® Times Cited:2
Deposited By:Information and Communication Technology
Deposited On:2014-02-22
Last Modified:2018-03-27

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