Abstract

Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skyline-join algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this paper, we aim to address these shortcomings by proposing two novel skyline-join algorithms, namely skyline-sensitive join (S 2J) and symmetric skyline-sensitive join (S 3J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LR-pruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the state-of-the-art skyline-join techniques.

Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series
Pages252-263
Number of pages12
DOIs
StatePublished - 2012
Event15th International Conference on Extending Database Technology, EDBT 2012 - Berlin, Germany
Duration: Mar 27 2012Mar 30 2012

Other

Other15th International Conference on Extending Database Technology, EDBT 2012
CountryGermany
CityBerlin
Period3/27/123/30/12

Fingerprint

Processing

Keywords

  • skyline-aware join processing

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Nagendra, M., & Candan, K. (2012). Skyline-sensitive joins with LR-pruning. In ACM International Conference Proceeding Series (pp. 252-263) https://doi.org/10.1145/2247596.2247627

Skyline-sensitive joins with LR-pruning. / Nagendra, Mithila; Candan, Kasim.

ACM International Conference Proceeding Series. 2012. p. 252-263.

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

Nagendra, M & Candan, K 2012, Skyline-sensitive joins with LR-pruning. in ACM International Conference Proceeding Series. pp. 252-263, 15th International Conference on Extending Database Technology, EDBT 2012, Berlin, Germany, 3/27/12. https://doi.org/10.1145/2247596.2247627
Nagendra M, Candan K. Skyline-sensitive joins with LR-pruning. In ACM International Conference Proceeding Series. 2012. p. 252-263 https://doi.org/10.1145/2247596.2247627
Nagendra, Mithila ; Candan, Kasim. / Skyline-sensitive joins with LR-pruning. ACM International Conference Proceeding Series. 2012. pp. 252-263
@inproceedings{698f10229e674788a0edd799ca5d88dd,
title = "Skyline-sensitive joins with LR-pruning",
abstract = "Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skyline-join algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this paper, we aim to address these shortcomings by proposing two novel skyline-join algorithms, namely skyline-sensitive join (S 2J) and symmetric skyline-sensitive join (S 3J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LR-pruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the state-of-the-art skyline-join techniques.",
keywords = "skyline-aware join processing",
author = "Mithila Nagendra and Kasim Candan",
year = "2012",
doi = "10.1145/2247596.2247627",
language = "English (US)",
isbn = "9781450307901",
pages = "252--263",
booktitle = "ACM International Conference Proceeding Series",

}

TY - GEN

T1 - Skyline-sensitive joins with LR-pruning

AU - Nagendra, Mithila

AU - Candan, Kasim

PY - 2012

Y1 - 2012

N2 - Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skyline-join algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this paper, we aim to address these shortcomings by proposing two novel skyline-join algorithms, namely skyline-sensitive join (S 2J) and symmetric skyline-sensitive join (S 3J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LR-pruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the state-of-the-art skyline-join techniques.

AB - Efficient processing of skyline queries has been an area of growing interest. Most existing techniques assume that the skyline query is applied to a single data table. Unfortunately, this is not true in many applications where, due to the complexity of the schema, the skyline query may involve attributes belonging to multiple tables. Recently, various hybrid skyline-join algorithms have been proposed. However, the current proposals suffer from several drawbacks: they often need to scan the input tables exhaustively in order to obtain the set of skyline-join results; moreover, the pruning techniques employed to eliminate the tuples are largely based on expensive pairwise tuple-to-tuple comparisons. In this paper, we aim to address these shortcomings by proposing two novel skyline-join algorithms, namely skyline-sensitive join (S 2J) and symmetric skyline-sensitive join (S 3J), to process skyline queries over multiple tables. Our approaches compute the results using a novel layer/region pruning technique (LR-pruning) that prunes the join space in blocks as opposed to individual data points, thereby avoiding excessive pairwise point-to-point dominance checks. Furthermore, the S 3J algorithm utilizes an early stopping condition in order to successfully compute the skyline results by accessing only a subset of the input tables. We report extensive experimental results that confirm the advantages of the proposed algorithms over the state-of-the-art skyline-join techniques.

KW - skyline-aware join processing

UR - http://www.scopus.com/inward/record.url?scp=84863519538&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863519538&partnerID=8YFLogxK

U2 - 10.1145/2247596.2247627

DO - 10.1145/2247596.2247627

M3 - Conference contribution

SN - 9781450307901

SP - 252

EP - 263

BT - ACM International Conference Proceeding Series

ER -