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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Agrawal R, Imielinski T, Swami A (1993) Minig association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 207–216
Arawal R, Srikant R (1994) Fast algorithms for mining association rues in large databases. In: Proceedings of the 20th international conference on very large data bases, San Francisco, pp 487–499
Han J (1998) Toward on-line analytical mining in large databases. SIGMOD Rec 27(1):97–107
Ian H (2005) Data mining practical machine learning tools and techniques. Morgan Kaufmann, San Mateo
Jiawei H, Micheline K (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Mateo
Keim D (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph 7(1):100–107
Klein A, Lehner W (2009) Representing data quality in sensor data streaming environments. Proc ACM J Data Inform 1(2)
Liang X, Liang Y (2001) Applications of data mining in hydrology. In: Proceedings of the IEEE international conference on data mining, pp 617–620
Liu Q, Bai Q, Terhorst A (2010) Provenance-aware hydrological sensor web. In: The proceedings of hydroinformatics conference, Tianjin, China, pp. 1307–1315
Mark H, Eibe F, Geoffrey H, Bernhard P, Peter R, Ian HW (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18
Open Geospatial Consortium (2007) OGC sensor web enablement: overview and high level architecture. Technical Report OGC 07-165
Su F, Zhou C, Lyne V, Du Y, Shi W (2004) A data-mining approach to determine the spatio-temporal relationship between environmental factors and fish distribution. Ecol Model 174(4):421–431
About this article
Cite this article
Zhang, M., Kang, B.H. & Bai, Q. Discover and visualize association rules from sensor observations on the web. J Supercomput 65, 4–15 (2013). https://doi.org/10.1007/s11227-011-0697-y
- Sensors and sensor networks
- Web-based environmental monitoring
- Data mining
- Association rules
- Knowledge discovery
- Data presentation