A data mining approach to XML dissemination

Authors:
Xiaoling Wang;Martin Ester;Weining Qian;Aoying Zhou
Affiliations:
Software Engineering Institute, East China Normal University, China;School of Computing Science, Simon Fraser University, Burnaby, Canada;Software Engineering Institute, East China Normal University, China;Software Engineering Institute, East China Normal University, China
Venue:
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Year:
2010

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Abstract

Currently user's interests are expressed by XPath or XQuery queries in XML dissemination applications. These queries require a good knowledge of the structure and contents of the documents that will arrive; As well as knowledge of XQuery which few consumers will have. In some cases, where the distinction of relevant and irrelevant documents requires the consideration of a large number of features, the query may be impossible. This paper introduces a data mining approach to XML dissemination that uses a given document collection of the user to automatically learn a classifier modelling of his/her information needs. Also discussed are the corresponding optimization methods that allow a dissemination server to execute a massive number of classifiers simultaneously. The experimental evaluation of several real XML document sets demonstrates the accuracy and efficiency of the proposed approach.