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Partout: a distributed engine for efficient RDF processing

Authors:

Luis Galárraga,

Katja Hose,

Ralf SchenkelAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 267 - 268

https://doi.org/10.1145/2567948.2577302

Published: 07 April 2014 Publication History

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Abstract

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications relying on efficient query processing. Confronted with such a trend, existing centralized state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for fast RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log and allocating the fragments to hosts in a cluster of machines. Furthermore, Partout's query optimizer produces efficient query execution plans for ad-hoc SPARQL queries.

References

[1]

L. Galarraga, K. Hose, and R. Schenkel. Partout: A Distributed Engine for Efficient RDF Processing. CoRR, abs/1212.5636, 2012.

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A. Schwarte, P. Haase, K. Hose, R. Schenkel, and M. Schmidt. FedX: Optimization Techniques for Federated Query Processing on Linked Data. In ISWC, pages 601--616, 2011.

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Cited By

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Azzam APolleres AD. Fernández JAcosta M(2024)smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphsSemantic Web10.3233/SW-24357115:5(1791-1835)Online publication date: 9-Oct-2024
https://doi.org/10.3233/SW-243571
Al-Ghezi AWiese L(2024)Analyzing workload trends for boosting triple stores performanceInformation Systems10.1016/j.is.2024.102420125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102420
Peng PJi SÖzsu MZou L(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
https://doi.org/10.1007/s00778-024-00860-1
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Index Terms

Partout: a distributed engine for efficient RDF processing
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs

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Information & Contributors

Information

Published In

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Poster

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Azzam APolleres AD. Fernández JAcosta M(2024)smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphsSemantic Web10.3233/SW-24357115:5(1791-1835)Online publication date: 9-Oct-2024
https://doi.org/10.3233/SW-243571
Al-Ghezi AWiese L(2024)Analyzing workload trends for boosting triple stores performanceInformation Systems10.1016/j.is.2024.102420125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102420
Peng PJi SÖzsu MZou L(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
https://doi.org/10.1007/s00778-024-00860-1
Elzein NMajid MHashem IIbrahim AAbulfaraj ABinzagr F(2023)JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join TechniqueMathematics10.3390/math1105127511:5(1275)Online publication date: 6-Mar-2023
https://doi.org/10.3390/math11051275
Aebeloe CMontoya GHose K(2023)Optimizing SPARQL queries over decentralized knowledge graphsSemantic Web10.3233/SW-23343814:6(1121-1165)Online publication date: 13-Dec-2023
https://doi.org/10.3233/SW-233438
Bastos ASingh KNadgeri AHoffart JSingh MSuzumura T(2023)Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?Proceedings of the ACM Web Conference 202310.1145/3543507.3583308(2455-2466)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583308
Hassan MBansal S(2023)S3QLRDF: distributed SPARQL query processing using Apache Spark—a comparative performance studyDistributed and Parallel Databases10.1007/s10619-023-07422-441:3(191-231)Online publication date: 24-Jan-2023
https://doi.org/10.1007/s10619-023-07422-4
Tian CWang TZhan DChang YWu XChen CCai XTong ENiu W(2023)LNFGP: Local Node Fusion-Based Graph Partition by Greedy ClusteringKnowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence10.1007/978-981-99-7224-1_9(109-120)Online publication date: 28-Oct-2023
https://doi.org/10.1007/978-981-99-7224-1_9
Hose K(2023)Knowledge Engineering in the Era of Artificial IntelligenceAdvances in Databases and Information Systems10.1007/978-3-031-42914-9_1(3-15)Online publication date: 28-Aug-2023
https://doi.org/10.1007/978-3-031-42914-9_1
Chawla T(2022)Storage and Query Processing Architectures for RDF DataEncyclopedia of Data Science and Machine Learning10.4018/978-1-7998-9220-5.ch019(298-313)Online publication date: 14-Oct-2022
https://doi.org/10.4018/978-1-7998-9220-5.ch019
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