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ABSTRACT: Existing algorithms of mining frequent XML query patterns (XQPs) employ a candidate generate-and-test strategy. They involve
expensive candidate enumeration and costly tree-containment checking. Further, most of existing methods compute the frequencies
of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XQPs
transferred from user query logs. However, it is not straightforward to maintain such discovered frequent patterns in real
XML databases as there may be frequent updates that may not only invalidate some existing frequent query patterns but also
generate some new frequent query patterns. Therefore, a drawback of existing methods is that they are rather inefficient for
the evolution of transaction databases. To address above-mentioned problems, this paper proposes an efficient algorithm ESPRIT to mine frequent XQPs without costly tree-containment checking. ESPRIT transforms XML queries into sequences using a one-to-one mapping technique and mines the frequent sequences to generate frequent
XQPs. We propose two efficient incremental algorithms, ESPRIT-i and ESPRIT-i
+, to incrementally mine frequent XQPs. We devise several novel optimization techniques of query rewriting, cache lookup, and
cache replacement to improve the answerability and the hit rate of caching. We have implemented our algorithms and conducted
a set of experimental studies on various datasets. The experimental results demonstrate that our algorithms achieve high efficiency
and scalability and outperform state-of-the-art methods significantly.
Data Mining and Knowledge Discovery 04/2012; 18(3):472-516. · 1.54 Impact Factor
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IEEE Trans. Knowl. Data Eng. 01/2011; 23:1035-1049.
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J. Data and Information Quality. 01/2011; 2:10.
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IEICE Transactions. 01/2011; 94-D:1321-1324.
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IJISSS. 01/2011; 3:1-21.
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Inf. Syst. 01/2011; 36:248-266.
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Database Systems for Advanced Applications - 16th International Conference, DASFAA 2011, Hong Kong, China, April 22-25, 2011, Proceedings, Part I; 01/2011
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Inf. Syst. 01/2010; 35:186-203.
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ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010; 01/2010
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ABSTRACT: The rapid evolution of information and communication technology opens a wide spectrum of opportunities to change our surroundings into an Ambient Intelligent (AmI) world. AmI is a vision of future information society, where people are surrounded by a digital environment that is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user friendliness, user empowerment, and more effective service support, with an aim to bring information and communication technology to everyone, every home, every business, and every school, thus improving the quality of human life. AmI unprecedentedly enhances learning experiences by endowing the users with the opportunities of learning in context, a breakthrough from the traditional education settings. In this survey paper, we examine some major characteristics of an AmI learning environment. To deliver a feasible and effective solution to ambient learning, we overview a few latest developed enabling technologies in context awareness and interactive learning. Associated practices are meanwhile reported. We also describe our experience in designing and implementing a smart class prototype, which allows teachers to simultaneously instruct both local and remote students in a context-aware and natural way.
IEEE Transactions on Knowledge and Data Engineering 07/2009; · 1.66 Impact Factor
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IEEE Trans. Knowl. Data Eng. 01/2009; 21:910-924.
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Data Min. Knowl. Discov. 01/2009; 18:1-29.
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PVLDB. 01/2009; 2:25-36.
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Database Systems for Advanced Applications, 14th International Conference, DASFAA 2009, Brisbane, Australia, April 21-23, 2009. Proceedings; 01/2009
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Resource Discovery, Second International Workshop, RED 2009, Lyon, France, August 28, 2009. Revised Papers; 01/2009
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Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, 2009; 01/2009
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Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, April 20-24, 2009; 01/2009
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EDBT 2009, 12th International Conference on Extending Database Technology, Saint Petersburg, Russia, March 24-26, 2009, Proceedings; 01/2009
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4th IEEE Asia-Pacific Services Computing Conference, IEEE APSCC 2009, Singapore, December 7-11 2009, Proceedings; 01/2009
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Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, Providence, Rhode Island, USA, June 29 - July 2, 2009; 01/2009
