WFApprox: Approximate Window Functions Processing

Chunbo Lin; Jingdong Li; Xiaoling Wang; Xingjian Lu; Ji Zhang

doi:10.1007/978-3-030-59410-7_5

WFApprox: Approximate Window Functions Processing

Chunbo Lin
, Jingdong Li
, Xiaoling Wang
, Xingjian Lu^*
, Ji Zhang

^*Corresponding author for this work

School of Computer Science and Technology

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

Abstract

Window functions, despite being supported by all major database systems, are unable to keep up with the steeply growing size of data. Recently, some approximate query process (AQP) systems are proposed to deal with large and complex data in relational databases, which offer us a flexible trade-off between accuracy and efficiency. At the same time, Machine Learning has been adopted extensively to optimize databases due to its powerful ability in dealing with data. However, there have been few publications that consider using AQP techniques especially model-based methods to accelerate window functions processing. This work presents WFApprox, an AQP system based on Machine Learning models aims at efficiently providing an approximate answer for window functions. WFApprox uses Machine Learning models instead of massive data for query answering. Our experimental evaluation shows that WFApprox significantly outperforms the mainstream database systems over TPC-H benchmark.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
Editors	Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	72-87
Number of pages	16
ISBN (Print)	9783030594091
DOIs	https://doi.org/10.1007/978-3-030-59410-7_5
State	Published - 2020
Event	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 - Jeju, Korea, Republic of Duration: 24 Sep 2020 → 27 Sep 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12112 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Country/Territory	Korea, Republic of
City	Jeju
Period	24/09/20 → 27/09/20

Keywords

Approximate query processing
Machine learning
Window functions

Access to Document

10.1007/978-3-030-59410-7_5

Cite this

Lin, C., Li, J., Wang, X., Lu, X., & Zhang, J. (2020). WFApprox: Approximate Window Functions Processing. In Y. Nah, B. Cui, S.-W. Lee, J. X. Yu, Y.-S. Moon, & S. E. Whang (Eds.), Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings (pp. 72-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12112 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59410-7_5

Lin, Chunbo ; Li, Jingdong ; Wang, Xiaoling et al. / WFApprox : Approximate Window Functions Processing. Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. editor / Yunmook Nah ; Bin Cui ; Sang-Won Lee ; Jeffrey Xu Yu ; Yang-Sae Moon ; Steven Euijong Whang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 72-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{357083327bbe4461b7126aec0e91bbf3,

title = "WFApprox: Approximate Window Functions Processing",

abstract = "Window functions, despite being supported by all major database systems, are unable to keep up with the steeply growing size of data. Recently, some approximate query process (AQP) systems are proposed to deal with large and complex data in relational databases, which offer us a flexible trade-off between accuracy and efficiency. At the same time, Machine Learning has been adopted extensively to optimize databases due to its powerful ability in dealing with data. However, there have been few publications that consider using AQP techniques especially model-based methods to accelerate window functions processing. This work presents WFApprox, an AQP system based on Machine Learning models aims at efficiently providing an approximate answer for window functions. WFApprox uses Machine Learning models instead of massive data for query answering. Our experimental evaluation shows that WFApprox significantly outperforms the mainstream database systems over TPC-H benchmark.",

keywords = "Approximate query processing, Machine learning, Window functions",

author = "Chunbo Lin and Jingdong Li and Xiaoling Wang and Xingjian Lu and Ji Zhang",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 ; Conference date: 24-09-2020 Through 27-09-2020",

year = "2020",

doi = "10.1007/978-3-030-59410-7\_5",

language = "英语",

isbn = "9783030594091",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "72--87",

editor = "Yunmook Nah and Bin Cui and Sang-Won Lee and Yu, \{Jeffrey Xu\} and Yang-Sae Moon and Whang, \{Steven Euijong\}",

booktitle = "Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings",

address = "德国",

}

Lin, C, Li, J, Wang, X , Lu, X & Zhang, J 2020, WFApprox: Approximate Window Functions Processing. in Y Nah, B Cui, S-W Lee, JX Yu, Y-S Moon & SE Whang (eds), Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12112 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 72-87, 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020, Jeju, Korea, Republic of, 24/09/20. https://doi.org/10.1007/978-3-030-59410-7_5

WFApprox: Approximate Window Functions Processing. / Lin, Chunbo; Li, Jingdong; Wang, Xiaoling et al.
Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. ed. / Yunmook Nah; Bin Cui; Sang-Won Lee; Jeffrey Xu Yu; Yang-Sae Moon; Steven Euijong Whang. Springer Science and Business Media Deutschland GmbH, 2020. p. 72-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12112 LNCS).

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

TY - GEN

T1 - WFApprox

T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020

AU - Lin, Chunbo

AU - Li, Jingdong

AU - Wang, Xiaoling

AU - Lu, Xingjian

AU - Zhang, Ji

PY - 2020

Y1 - 2020

N2 - Window functions, despite being supported by all major database systems, are unable to keep up with the steeply growing size of data. Recently, some approximate query process (AQP) systems are proposed to deal with large and complex data in relational databases, which offer us a flexible trade-off between accuracy and efficiency. At the same time, Machine Learning has been adopted extensively to optimize databases due to its powerful ability in dealing with data. However, there have been few publications that consider using AQP techniques especially model-based methods to accelerate window functions processing. This work presents WFApprox, an AQP system based on Machine Learning models aims at efficiently providing an approximate answer for window functions. WFApprox uses Machine Learning models instead of massive data for query answering. Our experimental evaluation shows that WFApprox significantly outperforms the mainstream database systems over TPC-H benchmark.

AB - Window functions, despite being supported by all major database systems, are unable to keep up with the steeply growing size of data. Recently, some approximate query process (AQP) systems are proposed to deal with large and complex data in relational databases, which offer us a flexible trade-off between accuracy and efficiency. At the same time, Machine Learning has been adopted extensively to optimize databases due to its powerful ability in dealing with data. However, there have been few publications that consider using AQP techniques especially model-based methods to accelerate window functions processing. This work presents WFApprox, an AQP system based on Machine Learning models aims at efficiently providing an approximate answer for window functions. WFApprox uses Machine Learning models instead of massive data for query answering. Our experimental evaluation shows that WFApprox significantly outperforms the mainstream database systems over TPC-H benchmark.

KW - Approximate query processing

KW - Machine learning

KW - Window functions

UR - https://www.scopus.com/pages/publications/85092107370

U2 - 10.1007/978-3-030-59410-7_5

DO - 10.1007/978-3-030-59410-7_5

M3 - 会议稿件

AN - SCOPUS:85092107370

SN - 9783030594091

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

SP - 72

EP - 87

BT - Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings

A2 - Nah, Yunmook

A2 - Cui, Bin

A2 - Lee, Sang-Won

A2 - Yu, Jeffrey Xu

A2 - Moon, Yang-Sae

A2 - Whang, Steven Euijong

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 24 September 2020 through 27 September 2020

ER -

Lin C, Li J, Wang X , Lu X, Zhang J. WFApprox: Approximate Window Functions Processing. In Nah Y, Cui B, Lee SW, Yu JX, Moon YS, Whang SE, editors, Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 72-87. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59410-7_5

WFApprox: Approximate Window Functions Processing

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Keywords

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