Efficient join-index-based spatial-join processing: A clustering approach

Shashi Shekhar; Chang Tien Lu; Sanjay Chawla; Sivakumar Ravada

doi:10.1109/TKDE.2002.1047776

Efficient join-index-based spatial-join processing: A clustering approach

Shashi Shekhar, Chang Tien Lu, Sanjay Chawla, Sivakumar Ravada

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for data sets which are updated infrequently. The I/O cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given a join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper bounds on the length of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms existing methods based on sorting and online-clustering heuristics.

Original language	English
Pages (from-to)	1400-1421
Number of pages	22
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	14
Issue number	6
DOIs	https://doi.org/10.1109/TKDE.2002.1047776
State	Published - Nov 2002
Externally published	Yes

Keywords

Join index
Join processing
Optimal page access sequence
Spatial join

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10.1109/TKDE.2002.1047776

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@article{4494a54e18c14a73850f2be6114727ab,

title = "Efficient join-index-based spatial-join processing: A clustering approach",

abstract = "A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for data sets which are updated infrequently. The I/O cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given a join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper bounds on the length of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms existing methods based on sorting and online-clustering heuristics.",

keywords = "Join index, Join processing, Optimal page access sequence, Spatial join",

author = "Shashi Shekhar and Lu, {Chang Tien} and Sanjay Chawla and Sivakumar Ravada",

note = "Funding Information: This work is sponsored in part by the US Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAH04-95-2-0003/contract number DAAH04-95-C-0008, the content of which does not necessarily reflect the position or the policy of the government and no official endorsement should be inferred. This work was also supported in part by US National Science Foundation grant #9631539. The authors would like to thank Kim Koffolt for improving the readability of this paper. We also would like to thank Xuan Liu, Xinhong Tan, and Weili Wu for their technical comments.",

year = "2002",

month = nov,

doi = "10.1109/TKDE.2002.1047776",

language = "English",

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journal = "IEEE Transactions on Knowledge and Data Engineering",

issn = "1041-4347",

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T2 - A clustering approach

AU - Shekhar, Shashi

AU - Lu, Chang Tien

AU - Chawla, Sanjay

AU - Ravada, Sivakumar

N1 - Funding Information: This work is sponsored in part by the US Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAH04-95-2-0003/contract number DAAH04-95-C-0008, the content of which does not necessarily reflect the position or the policy of the government and no official endorsement should be inferred. This work was also supported in part by US National Science Foundation grant #9631539. The authors would like to thank Kim Koffolt for improving the readability of this paper. We also would like to thank Xuan Liu, Xinhong Tan, and Weili Wu for their technical comments.

PY - 2002/11

Y1 - 2002/11

N2 - A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for data sets which are updated infrequently. The I/O cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given a join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper bounds on the length of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms existing methods based on sorting and online-clustering heuristics.

AB - A join-index is a data structure used for processing join queries in databases. Join-indices use precomputation techniques to speed up online query processing and are useful for data sets which are updated infrequently. The I/O cost of join computation using a join-index with limited buffer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given a join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper bounds on the length of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms existing methods based on sorting and online-clustering heuristics.

KW - Join index

KW - Join processing

KW - Optimal page access sequence

KW - Spatial join

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JO - IEEE Transactions on Knowledge and Data Engineering

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SN - 1041-4347

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Efficient join-index-based spatial-join processing: A clustering approach

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Keywords

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