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Query Optimisation for Data Mining in Peer-to-Peer Sensor Networks

Query Optimisation for Data Mining in Peer-to-Peer Sensor Networks

Mark Roantree, Alan F. Smeaton, Noel E. O’Connor, Vincent Andrieu, Nicolas Legeay, Fabrice Camous
Copyright: © 2010 |Pages: 23
ISBN13: 9781605663289|ISBN10: 160566328X|ISBN13 Softcover: 9781616922092|EISBN13: 9781605663296
DOI: 10.4018/978-1-60566-328-9.ch011
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MLA

Roantree, Mark, et al. "Query Optimisation for Data Mining in Peer-to-Peer Sensor Networks." Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, IGI Global, 2010, pp. 234-256. https://doi.org/10.4018/978-1-60566-328-9.ch011

APA

Roantree, M., Smeaton, A. F., O’Connor, N. E., Andrieu, V., Legeay, N., & Camous, F. (2010). Query Optimisation for Data Mining in Peer-to-Peer Sensor Networks. In A. Cuzzocrea (Ed.), Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 234-256). IGI Global. https://doi.org/10.4018/978-1-60566-328-9.ch011

Chicago

Roantree, Mark, et al. "Query Optimisation for Data Mining in Peer-to-Peer Sensor Networks." In Intelligent Techniques for Warehousing and Mining Sensor Network Data, edited by Alfredo Cuzzocrea, 234-256. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-328-9.ch011

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Abstract

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics and behaviour. This chapter proposes an approach and an implementation of semi-automated enrichment of raw sensor data, where the sensor data can come from a wide variety of sources. The authors extract semantics from the sensor data using their XSENSE processing architecture in a multi-stage analysis. The net result is that sensor data values are transformed into XML data so that well-established XML querying via XPATH and similar techniques can be followed. The authors then propose to distribute the XML data on a peer-to-peer configuration and show, through simulations, what the computational costs of executing queries on this P2P network, will be. This approach is validated approach through the use of an array of sensor data readings taken from a range of biometric sensor devices, fitted to movie-watchers as they watched Hollywood movies. These readings were synchronised with video and audio analysis of the actual movies themselves, where we automatically detect movie highlights, which the authors try to correlate with observed human reactions. The XSENSE architecture is used to semantically enrich both the biometric sensor readings and the outputs of video analysis, into one large sensor database. This chapter thus presents and validates a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor data management which is a necessary step in supporting data mining from sensor networks.

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