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Context aware discovery in web data through anomaly detection

Context aware discovery in web data through anomaly detection Context enables more accurate searches on the enormous information available on the web by setting the boundaries within which we can transition from data to relevant information. This paper describes a technique to analyse data extracted from the web and generate a contextual model that seamlessly combines data elements of a domain to provide the most accurate information to the user. The discovery of anomalies is of particular interest, since they may not be clearly evident without context information of a specific domain. A generic system design for extracting web data and generating a contextual model for any domain is presented. Contextual information and semantic techniques are used in a prototype system for the identification of potential threats associated with cargo shipments from the contextual perspective of relevant US federal agencies. An experimental evaluation shows that this technique increases precision of results. Keywords: context; semantics; anomaly detection; web-data extraction; web engineering; databases. Reference to this paper should be made as follows: Tambe, R., Karabatis, G. and Janeja, V.P. (2015) `Context aware discovery in web data through anomaly detection', Int. J. Web Engineering and Technology, Vol. 10, No. 1, pp.3­30. Biographical notes: Ruman Tambe received his Master's degree in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Engineering and Technology Inderscience Publishers

Context aware discovery in web data through anomaly detection

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1476-1289
eISSN
1741-9212
DOI
10.1504/IJWET.2015.069348
Publisher site
See Article on Publisher Site

Abstract

Context enables more accurate searches on the enormous information available on the web by setting the boundaries within which we can transition from data to relevant information. This paper describes a technique to analyse data extracted from the web and generate a contextual model that seamlessly combines data elements of a domain to provide the most accurate information to the user. The discovery of anomalies is of particular interest, since they may not be clearly evident without context information of a specific domain. A generic system design for extracting web data and generating a contextual model for any domain is presented. Contextual information and semantic techniques are used in a prototype system for the identification of potential threats associated with cargo shipments from the contextual perspective of relevant US federal agencies. An experimental evaluation shows that this technique increases precision of results. Keywords: context; semantics; anomaly detection; web-data extraction; web engineering; databases. Reference to this paper should be made as follows: Tambe, R., Karabatis, G. and Janeja, V.P. (2015) `Context aware discovery in web data through anomaly detection', Int. J. Web Engineering and Technology, Vol. 10, No. 1, pp.3­30. Biographical notes: Ruman Tambe received his Master's degree in

Journal

International Journal of Web Engineering and TechnologyInderscience Publishers

Published: Jan 1, 2015

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