Article

Online mining of frequent query trees over XML data streams

Authors:

Hua-Fu Li,

Man-Kwan Shan,

Suh-Yin LeeAuthors Info & Claims

WWW '06: Proceedings of the 15th international conference on World Wide Web

Pages 959 - 960

https://doi.org/10.1145/1135777.1135964

Published: 23 May 2006 Publication History

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Abstract

In this paper, we proposed an online algorithm, called FQT-Stream (Frequent Query Trees of Streams), to mine the set of all frequent tree patterns over a continuous XML data stream. A new numbering method is proposed to represent the tree structure of a XML query tree. An effective sub-tree numeration approach is developed to extract the essential information from the XML data stream. The extracted information is stored in an effective summary data structure. Frequent query trees are mined from the current summary data structure by a depth-first-search manner.

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Cited By

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Bifet AGavaldà R(2011)Mining frequent closed trees in evolving data streamsIntelligent Data Analysis10.5555/1937721.193772415:1(29-48)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/1937721.1937724
Suchanek FVarde ANayak RSenellart P(2011)The hidden web, XML and the Semantic WebProceedings of the 14th International Conference on Extending Database Technology10.1145/1951365.1951433(534-537)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1951365.1951433
Bifet A(2010)Adaptive Stream MiningProceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams10.5555/1735125.1735127(1-212)Online publication date: 27-Jul-2010
https://dl.acm.org/doi/10.5555/1735125.1735127
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Index Terms

Online mining of frequent query trees over XML data streams
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

WWW '06: Proceedings of the 15th international conference on World Wide Web

May 2006

1102 pages

ISBN:1595933239

DOI:10.1145/1135777

General Chairs:
Leslie Carr
University of Southampton
,
David De Roure
University of Southampton
,
Arun Iyengar
IBM Research
,
Program Chairs:
Carole Goble
University of Manchester, UK
,
Mike Dahlin
University of Texas at Austin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2006

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WWW06

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WWW06: The 15th International World Wide Web Conference 2006

May 23 - 26, 2006

Edinburgh, Scotland

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Bifet AGavaldà R(2011)Mining frequent closed trees in evolving data streamsIntelligent Data Analysis10.5555/1937721.193772415:1(29-48)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/1937721.1937724
Suchanek FVarde ANayak RSenellart P(2011)The hidden web, XML and the Semantic WebProceedings of the 14th International Conference on Extending Database Technology10.1145/1951365.1951433(534-537)Online publication date: 21-Mar-2011
https://dl.acm.org/doi/10.1145/1951365.1951433
Bifet A(2010)Adaptive Stream MiningProceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams10.5555/1735125.1735127(1-212)Online publication date: 27-Jul-2010
https://dl.acm.org/doi/10.5555/1735125.1735127
Bifet AGavaldà RLi YLiu BSarawagi S(2008)Mining adaptively frequent closed unlabeled rooted trees in data streamsProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1401890.1401900(34-42)Online publication date: 24-Aug-2008
https://dl.acm.org/doi/10.1145/1401890.1401900

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DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

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Mining frequent patterns from dynamic data streams with data load management