Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39372
Title: Approximate counting of frequent query patterns over XQuery stream
Authors: Yang, L.H. 
Lee, M.L. 
Hsu, W. 
Keywords: Stream mining
Tree mining
XML query pattern
Issue Date: 2004
Source: Yang, L.H.,Lee, M.L.,Hsu, W. (2004). Approximate counting of frequent query patterns over XQuery stream. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2973 : 75-87. ScholarBank@NUS Repository.
Abstract: One efficient approach to improve the performance of XML management systems is to cache the frequently retrieved results. This entails the discovery of frequent query patterns that are issued by users. In this paper, we model user queries as a stream of XML query pattern trees and mine for frequent query patterns in a batch-wise manner. We design a novel data structure called D-GQPT to merge the pattern trees of the batches seen so far, and to dynamically mark the active portion of the current batch. With the D-GQPT, we are able to limit the enumeration of candidate trees to only the currently active pattern trees. We also design a summary data structure called ECTree to incrementally compute the frequent tree patterns over the query stream. Based on the above two constructs, we present the frequent query pattern mining algorithm called AppXQSMiner over the XML query stream. Experiment results show that the proposed approach is both efficient and scalable. © Springer-Verlag 2004.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/39372
ISSN: 03029743
Appears in Collections:Staff Publications

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