Matches in Ghent University Academic Bibliography for { <https://biblio.ugent.be/publication/01HSDG8C7GN18E6PP4QVB4TN9K> ?p ?o. }
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- 01HSDG8C7GN18E6PP4QVB4TN9K classification C1.
- 01HSDG8C7GN18E6PP4QVB4TN9K date "2023".
- 01HSDG8C7GN18E6PP4QVB4TN9K language "eng".
- 01HSDG8C7GN18E6PP4QVB4TN9K type conference.
- 01HSDG8C7GN18E6PP4QVB4TN9K hasPart 01HSDGAMNV74TRP8573CRM9JCH.pdf.
- 01HSDG8C7GN18E6PP4QVB4TN9K hasPart 01HSDGBG3K7TZ82T6X50RM5RB3.pdf.
- 01HSDG8C7GN18E6PP4QVB4TN9K subject "Technology and Engineering".
- 01HSDG8C7GN18E6PP4QVB4TN9K doi "10.1007/978-3-031-43458-7_8".
- 01HSDG8C7GN18E6PP4QVB4TN9K isbn "9783031434570".
- 01HSDG8C7GN18E6PP4QVB4TN9K isbn "9783031434587".
- 01HSDG8C7GN18E6PP4QVB4TN9K issn "0302-9743".
- 01HSDG8C7GN18E6PP4QVB4TN9K issn "1611-3349".
- 01HSDG8C7GN18E6PP4QVB4TN9K presentedAt urn:uuid:10f27156-56b2-42ed-9b94-909dd99eb738.
- 01HSDG8C7GN18E6PP4QVB4TN9K volume "13998".
- 01HSDG8C7GN18E6PP4QVB4TN9K 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.".
- 01HSDG8C7GN18E6PP4QVB4TN9K author 115DF312-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HSDG8C7GN18E6PP4QVB4TN9K author 2C0A9B2A-F0EE-11E1-A9DE-61C894A0A6B4.
- 01HSDG8C7GN18E6PP4QVB4TN9K author c932e1a5-95f1-11ed-9897-c72ef345eb55.
- 01HSDG8C7GN18E6PP4QVB4TN9K author urn:uuid:2ab84498-a635-4383-af78-9352e25965fd.
- 01HSDG8C7GN18E6PP4QVB4TN9K dateCreated "2024-03-20T08:39:45Z".
- 01HSDG8C7GN18E6PP4QVB4TN9K dateModified "2024-11-28T00:18:30Z".
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:0476ae1b-b49b-4e05-9773-63a0f68c7ccf.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:19215040-8404-429e-9fda-668ff486fe7c.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:278348be-e06f-4a8c-839f-a5d66338acf1.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:34686e79-e447-4e8a-b6d2-e5a03ca2d206.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:b1739d7d-f68c-4804-ba47-b6eb40557493.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:b5d75d4f-0141-4d7d-80f0-45dfa78b8e2e.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:b73e6385-0df4-4507-8b2c-bc8b320a749d.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:c1bd8935-5505-4052-b0e4-c8ed9a44bb89.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:cb052502-cedf-4fff-87aa-d6a414f603d1.
- 01HSDG8C7GN18E6PP4QVB4TN9K editor urn:uuid:edef3c4e-17d4-4db8-9451-1bdbd86825e4.
- 01HSDG8C7GN18E6PP4QVB4TN9K name "Reinforcement learning-based SPARQL join ordering optimizer".
- 01HSDG8C7GN18E6PP4QVB4TN9K pagination urn:uuid:1bcb2127-927f-4ab0-a580-a374db47f1e8.
- 01HSDG8C7GN18E6PP4QVB4TN9K publisher urn:uuid:a2ae0b40-1709-4f3d-858c-b4a6a1de57ed.
- 01HSDG8C7GN18E6PP4QVB4TN9K sameAs LU-01HSDG8C7GN18E6PP4QVB4TN9K.
- 01HSDG8C7GN18E6PP4QVB4TN9K sourceOrganization urn:uuid:305e3c85-ab79-4924-b0e1-a87f048b5ab0.
- 01HSDG8C7GN18E6PP4QVB4TN9K sourceOrganization urn:uuid:fbcfca9e-c102-45b2-9ce0-9f8e633feb4a.
- 01HSDG8C7GN18E6PP4QVB4TN9K type C1.