@inproceedings{06d9299c7dd748b49d8f3be94f03c185,
title = "A data mining approach to XML dissemination",
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.",
keywords = "XML Classification, XML dissemination, feature vector, frequent structural pattern, pattern matching",
author = "Xiaoling Wang and Martin Ester and Weining Qian and Aoying Zhou",
year = "2010",
doi = "10.1007/978-3-642-17616-6\_40",
language = "英语",
isbn = "3642176151",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "442--455",
booktitle = "Web Information Systems Engineering, WISE 2010 - 11th International Conference, Proceedings",
note = "11th International Conference on Web Information Systems Engineering, WISE 2010 ; Conference date: 12-12-2010 Through 14-12-2010",
}