BSkyTree: Scalable skyline computation using a balanced pivot selection

Jongwuk Lee, Seung Won Hwang

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

36 Citations (Scopus)

Abstract

Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings
Pages195-206
Number of pages12
DOIs
Publication statusPublished - 2010 May 19
Event13th International Conference on Extending Database Technology: Advances in Database Technology - EDBT 2010 - Lausanne, Switzerland
Duration: 2010 Mar 222010 Mar 26

Publication series

NameAdvances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings

Other

Other13th International Conference on Extending Database Technology: Advances in Database Technology - EDBT 2010
CountrySwitzerland
CityLausanne
Period10/3/2210/3/26

Fingerprint

Costs

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Cite this

Lee, J., & Hwang, S. W. (2010). BSkyTree: Scalable skyline computation using a balanced pivot selection. In Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings (pp. 195-206). (Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings). https://doi.org/10.1145/1739041.1739067
Lee, Jongwuk ; Hwang, Seung Won. / BSkyTree : Scalable skyline computation using a balanced pivot selection. Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings. 2010. pp. 195-206 (Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings).
@inproceedings{ed1a401f27b9423ca6f99357a47a4528,
title = "BSkyTree: Scalable skyline computation using a balanced pivot selection",
abstract = "Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude.",
author = "Jongwuk Lee and Hwang, {Seung Won}",
year = "2010",
month = "5",
day = "19",
doi = "10.1145/1739041.1739067",
language = "English",
isbn = "9781605589459",
series = "Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings",
pages = "195--206",
booktitle = "Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings",

}

Lee, J & Hwang, SW 2010, BSkyTree: Scalable skyline computation using a balanced pivot selection. in Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings. Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings, pp. 195-206, 13th International Conference on Extending Database Technology: Advances in Database Technology - EDBT 2010, Lausanne, Switzerland, 10/3/22. https://doi.org/10.1145/1739041.1739067

BSkyTree : Scalable skyline computation using a balanced pivot selection. / Lee, Jongwuk; Hwang, Seung Won.

Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings. 2010. p. 195-206 (Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings).

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

TY - GEN

T1 - BSkyTree

T2 - Scalable skyline computation using a balanced pivot selection

AU - Lee, Jongwuk

AU - Hwang, Seung Won

PY - 2010/5/19

Y1 - 2010/5/19

N2 - Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude.

AB - Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude.

UR - http://www.scopus.com/inward/record.url?scp=77952265089&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952265089&partnerID=8YFLogxK

U2 - 10.1145/1739041.1739067

DO - 10.1145/1739041.1739067

M3 - Conference contribution

AN - SCOPUS:77952265089

SN - 9781605589459

T3 - Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings

SP - 195

EP - 206

BT - Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings

ER -

Lee J, Hwang SW. BSkyTree: Scalable skyline computation using a balanced pivot selection. In Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings. 2010. p. 195-206. (Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings). https://doi.org/10.1145/1739041.1739067