Keywords filtering over probabilistic XML data

Authors:
Chenjing Zhang;Le Chang;Chaofeng Sha;Xiaoling Wang;Aoying Zhou
Affiliations:
College of Information Technology, Shanghai Ocean University, China and School of Computer Science, Fudan University, China;Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University, China;School of Computer Science, Fudan University, China;Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University, China;Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University, China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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Abstract

Probabilistic XML data is widely used in many web applications. Recent work has been mostly focused on structured query over probabilistic XML data. A few of work has been done about keyword query. However only the independent and the mutually-exclusive relationship among sibling nodes are discussed. This paper addresses the problem of keyword filtering over probabilistic XML data, and we propose PrXML{exp, ind, mux} model to represent a more general relationship among XML sibling nodes, for keywords filtering over probabilistic XML data. kdptab is defined as keyword distribution probability table of one subtree. The Dot product, Cartesian product, and addition operation of kdptab are also defined. In PrXML{exp, ind, mux} model, XML document is scanned bottom-up and achieve keyword filtering based on SLCA semantics efficiently in our method. Finally, the features and efficiency of our method are evaluated with extensive experimental results.