Reinforcement Learning-Based SPARQL Join Ordering Optimizer

Ruben Eschauzier; Ruben Taelman; Meike Morren; Ruben Verborgh

doi:10.1007/978-3-031-43458-7_8

Reinforcement Learning-Based SPARQL Join Ordering Optimizer

Ruben Eschauzier^*
, Ruben Taelman
, Meike Morren
, Ruben Verborgh

^*Corresponding author for this work

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

113 Downloads (Pure)

Abstract

In recent years, relational databases successfully leverage reinforcement learning to optimize query plans. For graph databases and RDF quad stores, such research has been limited, so there is a need to understand the impact of reinforcement learning techniques. We explore a reinforcement learning-based join plan optimizer that we design specifically for optimizing join plans during SPARQL query planning. This paper presents key aspects of this method and highlights open research problems. We argue that while we can reuse aspects of relational database optimization, SPARQL query optimization presents unique challenges not encountered in relational databases. Nevertheless, initial benchmarks show promising results that warrant further exploration.

Original language	English
Title of host publication	The Semantic Web: ESWC 2023 Satellite Events
Subtitle of host publication	Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings
Editors	Catia Pesquita, Hala Skaf-Molli, Vasilis Efthymiou, Sabrina Kirrane, Axel Ngonga, Diego Collarana, Renato Cerqueira, Mehwish Alam, Cassia Trojahn, Sven Hertling
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	43-47
Number of pages	5
ISBN (Electronic)	9783031434587
ISBN (Print)	9783031434570
DOIs	https://doi.org/10.1007/978-3-031-43458-7_8
Publication status	Published - 2023
Event	20th Extended Semantic Web Conference, ESWC 2023 - Hersonissos, Greece Duration: 28 May 2023 → 1 Jun 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13998 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Extended Semantic Web Conference, ESWC 2023
Country/Territory	Greece
City	Hersonissos
Period	28/05/23 → 1/06/23

Bibliographical note

Funding Information:
This work is supported by SolidLab Vlaanderen (Flemish Government, EWI and RRF project VV023/10). Ruben Taelman is a postdoctoral fellow of the Research Foundation-Flanders (FWO) (1274521N).

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.

Funding

This work is supported by SolidLab Vlaanderen (Flemish Government, EWI and RRF project VV023/10). Ruben Taelman is a postdoctoral fellow of the Research Foundation-Flanders (FWO) (1274521N).

Keywords

Join Order Optimization
Machine Learning
Reinforcement learning
SPARQL

Access to Document

10.1007/978-3-031-43458-7_8

Reinforcement Learning-Based SPARQL Join Ordering OptimizerFinal published version, 150 KBLicence: Article 25fa Dutch Copyright Act

Persistent URL (handle)

Persistent link

Cite this

Eschauzier, R., Taelman, R., Morren, M., & Verborgh, R. (2023). Reinforcement Learning-Based SPARQL Join Ordering Optimizer. In C. Pesquita, H. Skaf-Molli, V. Efthymiou, S. Kirrane, A. Ngonga, D. Collarana, R. Cerqueira, M. Alam, C. Trojahn, & S. Hertling (Eds.), The Semantic Web: ESWC 2023 Satellite Events : Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings (pp. 43-47). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13998 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43458-7_8

Eschauzier, Ruben ; Taelman, Ruben ; Morren, Meike et al. / Reinforcement Learning-Based SPARQL Join Ordering Optimizer. The Semantic Web: ESWC 2023 Satellite Events : Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings. editor / Catia Pesquita ; Hala Skaf-Molli ; Vasilis Efthymiou ; Sabrina Kirrane ; Axel Ngonga ; Diego Collarana ; Renato Cerqueira ; Mehwish Alam ; Cassia Trojahn ; Sven Hertling. Springer Science and Business Media Deutschland GmbH, 2023. pp. 43-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e2b8ca98f82f43f0841cc2c129470cc0,

title = "Reinforcement Learning-Based SPARQL Join Ordering Optimizer",

abstract = "In recent years, relational databases successfully leverage reinforcement learning to optimize query plans. For graph databases and RDF quad stores, such research has been limited, so there is a need to understand the impact of reinforcement learning techniques. We explore a reinforcement learning-based join plan optimizer that we design specifically for optimizing join plans during SPARQL query planning. This paper presents key aspects of this method and highlights open research problems. We argue that while we can reuse aspects of relational database optimization, SPARQL query optimization presents unique challenges not encountered in relational databases. Nevertheless, initial benchmarks show promising results that warrant further exploration.",

keywords = "Join Order Optimization, Machine Learning, Reinforcement learning, SPARQL",

author = "Ruben Eschauzier and Ruben Taelman and Meike Morren and Ruben Verborgh",

note = "Funding Information: This work is supported by SolidLab Vlaanderen (Flemish Government, EWI and RRF project VV023/10). Ruben Taelman is a postdoctoral fellow of the Research Foundation-Flanders (FWO) (1274521N). Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.; 20th Extended Semantic Web Conference, ESWC 2023 ; Conference date: 28-05-2023 Through 01-06-2023",

year = "2023",

doi = "10.1007/978-3-031-43458-7\_8",

language = "English",

isbn = "9783031434570",

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 = "43--47",

editor = "Catia Pesquita and Hala Skaf-Molli and Vasilis Efthymiou and Sabrina Kirrane and Axel Ngonga and Diego Collarana and Renato Cerqueira and Mehwish Alam and Cassia Trojahn and Sven Hertling",

booktitle = "The Semantic Web: ESWC 2023 Satellite Events",

address = "Germany",

}

Eschauzier, R, Taelman, R, Morren, M & Verborgh, R 2023, Reinforcement Learning-Based SPARQL Join Ordering Optimizer. in C Pesquita, H Skaf-Molli, V Efthymiou, S Kirrane, A Ngonga, D Collarana, R Cerqueira, M Alam, C Trojahn & S Hertling (eds), The Semantic Web: ESWC 2023 Satellite Events : Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13998 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 43-47, 20th Extended Semantic Web Conference, ESWC 2023, Hersonissos, Greece, 28/05/23. https://doi.org/10.1007/978-3-031-43458-7_8

Reinforcement Learning-Based SPARQL Join Ordering Optimizer. / Eschauzier, Ruben; Taelman, Ruben; Morren, Meike et al.
The Semantic Web: ESWC 2023 Satellite Events : Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings. ed. / Catia Pesquita; Hala Skaf-Molli; Vasilis Efthymiou; Sabrina Kirrane; Axel Ngonga; Diego Collarana; Renato Cerqueira; Mehwish Alam; Cassia Trojahn; Sven Hertling. Springer Science and Business Media Deutschland GmbH, 2023. p. 43-47 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13998 LNCS).

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

TY - GEN

T1 - Reinforcement Learning-Based SPARQL Join Ordering Optimizer

AU - Eschauzier, Ruben

AU - Taelman, Ruben

AU - Morren, Meike

AU - Verborgh, Ruben

N1 - Funding Information: This work is supported by SolidLab Vlaanderen (Flemish Government, EWI and RRF project VV023/10). Ruben Taelman is a postdoctoral fellow of the Research Foundation-Flanders (FWO) (1274521N). Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023.

PY - 2023

Y1 - 2023

N2 - In recent years, relational databases successfully leverage reinforcement learning to optimize query plans. For graph databases and RDF quad stores, such research has been limited, so there is a need to understand the impact of reinforcement learning techniques. We explore a reinforcement learning-based join plan optimizer that we design specifically for optimizing join plans during SPARQL query planning. This paper presents key aspects of this method and highlights open research problems. We argue that while we can reuse aspects of relational database optimization, SPARQL query optimization presents unique challenges not encountered in relational databases. Nevertheless, initial benchmarks show promising results that warrant further exploration.

AB - In recent years, relational databases successfully leverage reinforcement learning to optimize query plans. For graph databases and RDF quad stores, such research has been limited, so there is a need to understand the impact of reinforcement learning techniques. We explore a reinforcement learning-based join plan optimizer that we design specifically for optimizing join plans during SPARQL query planning. This paper presents key aspects of this method and highlights open research problems. We argue that while we can reuse aspects of relational database optimization, SPARQL query optimization presents unique challenges not encountered in relational databases. Nevertheless, initial benchmarks show promising results that warrant further exploration.

KW - Join Order Optimization

KW - Machine Learning

KW - Reinforcement learning

KW - SPARQL

UR - https://www.scopus.com/pages/publications/85176017847

UR - https://www.scopus.com/pages/publications/85176017847#tab=citedBy

U2 - 10.1007/978-3-031-43458-7_8

DO - 10.1007/978-3-031-43458-7_8

M3 - Conference contribution

AN - SCOPUS:85176017847

SN - 9783031434570

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

SP - 43

EP - 47

BT - The Semantic Web: ESWC 2023 Satellite Events

A2 - Pesquita, Catia

A2 - Skaf-Molli, Hala

A2 - Efthymiou, Vasilis

A2 - Kirrane, Sabrina

A2 - Ngonga, Axel

A2 - Collarana, Diego

A2 - Cerqueira, Renato

A2 - Alam, Mehwish

A2 - Trojahn, Cassia

A2 - Hertling, Sven

PB - Springer Science and Business Media Deutschland GmbH

T2 - 20th Extended Semantic Web Conference, ESWC 2023

Y2 - 28 May 2023 through 1 June 2023

ER -

Eschauzier R, Taelman R, Morren M, Verborgh R. Reinforcement Learning-Based SPARQL Join Ordering Optimizer. In Pesquita C, Skaf-Molli H, Efthymiou V, Kirrane S, Ngonga A, Collarana D, Cerqueira R, Alam M, Trojahn C, Hertling S, editors, The Semantic Web: ESWC 2023 Satellite Events : Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 43-47. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2023 Oct 21. doi: 10.1007/978-3-031-43458-7_8

Reinforcement Learning-Based SPARQL Join Ordering Optimizer

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this