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Efficient RDF Knowledge Graph Partitioning Using Querying Workload

Published: 02 December 2021 Publication History

Abstract

Data partitioning is an effective way to manage large datasets. While a broad range of RDF graph partitioning techniques has been proposed in previous works, little attention has been given to workload-aware RDF graph partitioning. In this paper, we propose two techniques that make use of the querying workload to detect the portions of RDF graphs that are often queried concurrently. Our techniques leverage predicate co-occurrences in SPARQL queries. By detecting highly co-occurring predicates, our techniques can keep data pertaining to these predicates in the same data partition. We evaluate the proposed partitioning techniques using various real-data and query benchmarks generated by the FEASIBLE SPARQL benchmark generation framework. Our evaluation results show the superiority of the proposed techniques in comparison to previous techniques in terms of better query runtime performances.

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Cited By

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  • (2024)Minimum motif-cut: a workload-aware RDF graph partitioning strategyThe VLDB Journal10.1007/s00778-024-00860-133:5(1517-1542)Online publication date: 8-Jul-2024
  • (2023)Optimizing SPARQL queries over decentralized knowledge graphsSemantic Web10.3233/SW-23343814:6(1121-1165)Online publication date: 13-Dec-2023
  • (2023)RDF Data Partitioning for Efficient SPARQL Query Processing with Spark SQLInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_12(92-106)Online publication date: 4-Dec-2023

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  1. Efficient RDF Knowledge Graph Partitioning Using Querying Workload

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    cover image ACM Conferences
    K-CAP '21: Proceedings of the 11th Knowledge Capture Conference
    December 2021
    300 pages
    ISBN:9781450384575
    DOI:10.1145/3460210
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 02 December 2021

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    Author Tags

    1. pcg
    2. pcm
    3. predicate co-occurrence
    4. querying workload
    5. rdf knowledge graph partitioning

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    • Research-article

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    • 3DFed; KnowGraphs

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    K-CAP '21
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    K-CAP '21: Knowledge Capture Conference
    December 2 - 3, 2021
    Virtual Event, USA

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    Overall Acceptance Rate 55 of 198 submissions, 28%

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    Cited By

    View all
    • (2024)Minimum motif-cut: a workload-aware RDF graph partitioning strategyThe VLDB Journal10.1007/s00778-024-00860-133:5(1517-1542)Online publication date: 8-Jul-2024
    • (2023)Optimizing SPARQL queries over decentralized knowledge graphsSemantic Web10.3233/SW-23343814:6(1121-1165)Online publication date: 13-Dec-2023
    • (2023)RDF Data Partitioning for Efficient SPARQL Query Processing with Spark SQLInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_12(92-106)Online publication date: 4-Dec-2023

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