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.
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Acknowledgments
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).
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Eschauzier, R., Taelman, R., Morren, M., Verborgh, R. (2023). Reinforcement Learning-Based SPARQL Join Ordering Optimizer. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_8
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DOI: https://doi.org/10.1007/978-3-031-43458-7_8
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