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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Wang, X., Ester, M., Qian, W., & Zhou, A. (2010). A data mining approach to XML dissemination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6488 LNCS, pp. 442–455). https://doi.org/10.1007/978-3-642-17616-6_40
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