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Monitoring continuous band-join queries over dynamic data

Publication ,  Journal Article
Agarwal, PK; Xie, J; Yang, J; Yu, H
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
December 1, 2005

A continuous query is a standing query over a dynamic data set whose query result needs to be constantly updated as new data arrive. We consider the problem of constructing a data structure on a set of continuous band-join queries over two data sets R and S, where each band-join query asks for reporting the set {(r, s) ∈R × S | a ≤r - s ≤ b} for some parameters a and b, so that given a data update in R or S, one can quickly identify the subset of continuous queries whose results are affected by the update, and compute changes to these results. We present the first nontrivial data structure for this problem that simultaneously achieves subquadratic space and sublinear query time. This is achieved by first decomposing the original problem into two independent subproblems, and then carefully designing data structures suitable for each case, by exploiting the particular structure in each subproblem. A key step in the above construction is a data structure whose performance increases with the degree of clusteredness of the band-joins being indexed. We believe that this structure is of independent interest and should have broad impact in practice. We present the details in [1]. © Springer-Verlag Berlin Heidelberg 2005.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2005

Volume

3827 LNCS

Start / End Page

349 / 359

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Agarwal, P. K., Xie, J., Yang, J., & Yu, H. (2005). Monitoring continuous band-join queries over dynamic data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3827 LNCS, 349–359. https://doi.org/10.1007/11602613_36
Agarwal, P. K., J. Xie, J. Yang, and H. Yu. “Monitoring continuous band-join queries over dynamic data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3827 LNCS (December 1, 2005): 349–59. https://doi.org/10.1007/11602613_36.
Agarwal PK, Xie J, Yang J, Yu H. Monitoring continuous band-join queries over dynamic data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005 Dec 1;3827 LNCS:349–59.
Agarwal, P. K., et al. “Monitoring continuous band-join queries over dynamic data.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3827 LNCS, Dec. 2005, pp. 349–59. Scopus, doi:10.1007/11602613_36.
Agarwal PK, Xie J, Yang J, Yu H. Monitoring continuous band-join queries over dynamic data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005 Dec 1;3827 LNCS:349–359.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

December 1, 2005

Volume

3827 LNCS

Start / End Page

349 / 359

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences