Efficient evaluation of partially-dimensional range queries using adaptive R*-tree

Yaokai Feng; Akifumi Makinouchi

Efficient evaluation of partially-dimensional range queries using adaptive R*-tree

Yaokai Feng, Akifumi Makinouchi

Real world robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.

Original language	English
Title of host publication	Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings
Publisher	Springer Verlag
Pages	687-696
Number of pages	10
Volume	4080 LNCS
ISBN (Print)	3540378715, 9783540378716
Publication status	Published - 2006
Event	17th International Conference on Database and Expert Systems Applications, DEXA 2006 - Krakow, Poland Duration: Sep 4 2006 → Sep 8 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4080 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	17th International Conference on Database and Expert Systems Applications, DEXA 2006
Country/Territory	Poland
City	Krakow
Period	9/4/06 → 9/8/06

All Science Journal Classification (ASJC) codes

Computer Science(all)
Theoretical Computer Science

Cite this

Feng, Y., & Makinouchi, A. (2006). Efficient evaluation of partially-dimensional range queries using adaptive R*-tree. In Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings (Vol. 4080 LNCS, pp. 687-696). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4080 LNCS). Springer Verlag.

Efficient evaluation of partially-dimensional range queries using adaptive R*-tree. / Feng, Yaokai; Makinouchi, Akifumi.

Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings. Vol. 4080 LNCS Springer Verlag, 2006. p. 687-696 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4080 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Feng, Y & Makinouchi, A 2006, Efficient evaluation of partially-dimensional range queries using adaptive R*-tree. in Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings. vol. 4080 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4080 LNCS, Springer Verlag, pp. 687-696, 17th International Conference on Database and Expert Systems Applications, DEXA 2006, Krakow, Poland, 9/4/06.

Feng Y, Makinouchi A. Efficient evaluation of partially-dimensional range queries using adaptive R*-tree. In Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings. Vol. 4080 LNCS. Springer Verlag. 2006. p. 687-696. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Feng, Yaokai ; Makinouchi, Akifumi. / Efficient evaluation of partially-dimensional range queries using adaptive R*-tree. Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings. Vol. 4080 LNCS Springer Verlag, 2006. pp. 687-696 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{92881729d42b4de7a51eb8677e15db36,

title = "Efficient evaluation of partially-dimensional range queries using adaptive R*-tree",

abstract = "This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.",

author = "Yaokai Feng and Akifumi Makinouchi",

year = "2006",

language = "English",

isbn = "3540378715",

volume = "4080 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "687--696",

booktitle = "Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings",

address = "Germany",

note = "17th International Conference on Database and Expert Systems Applications, DEXA 2006 ; Conference date: 04-09-2006 Through 08-09-2006",

}

TY - GEN

T1 - Efficient evaluation of partially-dimensional range queries using adaptive R*-tree

AU - Feng, Yaokai

AU - Makinouchi, Akifumi

PY - 2006

Y1 - 2006

N2 - This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.

AB - This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.

UR - http://www.scopus.com/inward/record.url?scp=33749415024&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749415024&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33749415024

SN - 3540378715

SN - 9783540378716

VL - 4080 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 687

EP - 696

BT - Database and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings

PB - Springer Verlag

T2 - 17th International Conference on Database and Expert Systems Applications, DEXA 2006

Y2 - 4 September 2006 through 8 September 2006

ER -

Efficient evaluation of partially-dimensional range queries using adaptive R*-tree

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this