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Issue No. 07 - July (2011 vol. 23)
ISSN: 1041-4347
pp: 1035-1049
Guoliang Li , Tsinghua Univsersity, Beijing
Jianhua Feng , Tsinghua University, Beijing
Jianyong Wang , Tsinghua University, Beijing
Lizhu Zhou , Tsinghua University, Beijing
This paper studies the problem of XML message brokering with user subscribed profiles of keyword queries and presents a KEyword-based XML Message Broker (KEMB) to address this problem. In contrast to traditional-path-expressions-based XML message brokers, KEMB stores a large number of user profiles, in the form of keyword queries, which capture the data requirement of users/applications, as opposed to path expressions, such as XPath/XQuery expressions. KEMB brings new challenges: 1) how to effectively identify relevant answers of keyword queries in XML data streams; and 2) how to efficiently answer large numbers of concurrent keyword queries. We adopt compact lowest common ancestors (CLCAs) to effectively identify relevant answers. We devise an automaton-based method to process large numbers of queries and devise an effective optimization strategy to enhance performance and scalability. We have implemented and evaluated KEMB on various data sets. The experimental results show that KEMB achieves high performance and scales very well.
Keyword search, XML data stream, XML message brokers, compact lowest common ancestor (CLCA).

G. Li, L. Zhou, J. Feng and J. Wang, "KEMB: A Keyword-Based XML Message Broker," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 1035-1049, 2010.
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