ReJOOSp: Reinforcement Learning for Join Order Optimization in SPARQL
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Published:2024-06-27
Issue:7
Volume:8
Page:71
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ISSN:2504-2289
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Container-title:Big Data and Cognitive Computing
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language:en
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Short-container-title:BDCC
Author:
Warnke Benjamin1ORCID, Martens Kevin1, Winker Tobias1, Groppe Sven1ORCID, Groppe Jinghua1, Adhiyaman Prasad2, Srinivasan Sruthi2ORCID, Krishnakumar Shridevi2ORCID
Affiliation:
1. Institute of Information Systems, University of Lübeck, 23562 Lübeck, Germany 2. Centre for Advanced Data Science, Vellore Institute of Technology, Chennai 600127, India
Abstract
The choice of a good join order plays an important role in the query performance of databases. However, determining the best join order is known to be an NP-hard problem with exponential growth with the number of joins. Because of this, nonlearning approaches to join order optimization have a longer optimization and execution time. In comparison, the models of machine learning, once trained, can construct optimized query plans very quickly. Several efforts have applied machine learning to optimize join order for SQL queries outperforming traditional approaches. In this work, we suggest a reinforcement learning technique for join optimization for SPARQL queries, ReJOOSp. SPARQL queries typically contain a much higher number of joins than SQL queries and so are more difficult to optimize. To evaluate ReJOOSp, we further develop a join order optimizer based on ReJOOSp and integrate it into the Semantic Web DBMS Luposdate3000. The evaluation of ReJOOSp shows its capability to significantly enhance query performance by achieving high-quality execution plans for a substantial portion of queries across synthetic and real-world datasets.
Funder
Deutsche Forschungsgemeinschaft German Federal Ministry of Education and Research within the funding program quantum technologies
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