QED

An efficient framework for temporal region query processing

Yi Hong Chu, Kun-Ta Chuang, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we explore a new problem of "temporal dense region query" to discover the dense regions in the constrainted time intervals which can be separated or not. A Querying tEmporal Dense Region framework (abbreviated as QED) proposed to deal with this problem consists of two phases: (1) an offline maintaining phase, to maintain the statistics of data by constructing a number of summarized structures, RF-trees; (2) an online query processing phase, to provide an efficient algorithm to execute queries on the RF-trees. The QED framework has the advantage that by using the summarized structures, RF-trees, the queries can be executed efficiently without accessing the raw data. In addition, a number of RF-trees can be merged with one another efficiently such that the queries will be executed efficiently on the combined RF-tree. As validated by our empirical studies, the QED framework performs very efficiently while producing the results of high quality.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages323-332
Number of pages10
Volume3518 LNAI
Publication statusPublished - 2005
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: 2005 May 182005 May 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CountryViet Nam
CityHanoi
Period05-05-1805-05-20

Fingerprint

Query processing
Query Processing
Statistics
Query
Empirical Study
Efficient Algorithms
Framework
Interval

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chu, Y. H., Chuang, K-T., & Chen, M. S. (2005). QED: An efficient framework for temporal region query processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 323-332). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).
Chu, Yi Hong ; Chuang, Kun-Ta ; Chen, Ming Syan. / QED : An efficient framework for temporal region query processing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. pp. 323-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "In this paper, we explore a new problem of {"}temporal dense region query{"} to discover the dense regions in the constrainted time intervals which can be separated or not. A Querying tEmporal Dense Region framework (abbreviated as QED) proposed to deal with this problem consists of two phases: (1) an offline maintaining phase, to maintain the statistics of data by constructing a number of summarized structures, RF-trees; (2) an online query processing phase, to provide an efficient algorithm to execute queries on the RF-trees. The QED framework has the advantage that by using the summarized structures, RF-trees, the queries can be executed efficiently without accessing the raw data. In addition, a number of RF-trees can be merged with one another efficiently such that the queries will be executed efficiently on the combined RF-tree. As validated by our empirical studies, the QED framework performs very efficiently while producing the results of high quality.",
author = "Chu, {Yi Hong} and Kun-Ta Chuang and Chen, {Ming Syan}",
year = "2005",
language = "English",
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}

Chu, YH, Chuang, K-T & Chen, MS 2005, QED: An efficient framework for temporal region query processing. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3518 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3518 LNAI, pp. 323-332, 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005, Hanoi, Viet Nam, 05-05-18.

QED : An efficient framework for temporal region query processing. / Chu, Yi Hong; Chuang, Kun-Ta; Chen, Ming Syan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. p. 323-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chu YH, Chuang K-T, Chen MS. QED: An efficient framework for temporal region query processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI. 2005. p. 323-332. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).