research-article

Scalable semantic web data management using vertical partitioning

Authors:

Daniel J. Abadi,

Samuel R. Madden,

Kate HollenbachAuthors Info & Claims

VLDB '07: Proceedings of the 33rd international conference on Very large data bases

Pages 411 - 422

Published: 23 September 2007 Publication History

Abstract

Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.

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Hendler JShadbolt NHall WBerners-Lee TWeitzner D(2023)Web Science: An Interdisciplinary Approach to Understanding the WebLinking the World’s Information10.1145/3591366.3591374(67-84)Online publication date: 5-Sep-2023
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Published In

cover image DL Hosted proceedings

VLDB '07: Proceedings of the 33rd international conference on Very large data bases

September 2007

1443 pages

ISBN:9781595936493

General Chairs:
Wolfgang Klas
University of Vienna, Austria
,
Erich J. Neuhold
Research Studio Digital Memory Engineering, Austria

Sponsors

Yahoo! Research
Google Inc.
SAP
Intel: Intel
Microsoft Research: Microsoft Research
ORACLE: ORACLE
Connex.cc
HP invent
WKO
IBM: IBM

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VLDB Endowment

Publication History

Published: 23 September 2007

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Research-article

Conference

VLDB '07

Sponsor:

Intel
Microsoft Research
ORACLE
IBM

VLDB '07: Proceedings of the 33rd International Conference on Very Large Data Bases

September 23 - 27, 2007

Vienna, Austria

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Cited By

Hu PMotik B(2024)Accurate Sampling-Based Cardinality Estimation for Complex Graph QueriesACM Transactions on Database Systems10.1145/368920949:3(1-46)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1145/3689209
Hendler JShadbolt NHall WBerners-Lee TWeitzner D(2023)Web Science: An Interdisciplinary Approach to Understanding the WebLinking the World’s Information10.1145/3591366.3591374(67-84)Online publication date: 5-Sep-2023
https://dl.acm.org/doi/10.1145/3591366.3591374
Yuan GLu JYan ZWu S(2023)A Survey on Mapping Semi-Structured Data and Graph Data to Relational DataACM Computing Surveys10.1145/356744455:10(1-38)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3567444
Yuan QYuan YWen ZWang HTang SChen HDuh WHuang HKato MMothe JPoblete B(2023)An Effective Framework for Enhancing Query Answering in a Heterogeneous Data LakeProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591637(770-780)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591637
Zhou Y(2022)Large-Scale Commodity Knowledge Organization and Intelligent Query OptimizationInternational Journal of Mobile Computing and Multimedia Communications10.4018/IJMCMC.29796513:1(1-25)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.4018/IJMCMC.297965
Theocharidis KLiagouris JMamoulis NBouros PTerrovitis M(2022)SRX: efficient management of spatial RDF dataThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00554-z28:5(703-733)Online publication date: 11-Mar-2022
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Gupta PMhedhbi ASalihoglu S(2021)Columnar storage and list-based processing for graph database management systemsProceedings of the VLDB Endowment10.14778/3476249.347629714:11(2491-2504)Online publication date: 27-Oct-2021
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Pandat AGupta NBhise M(2021)Load Balanced Semantic Aware Distributed RDF GraphProceedings of the 25th International Database Engineering & Applications Symposium10.1145/3472163.3472167(127-133)Online publication date: 14-Jul-2021
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Xu WCuré OCalvez P(2020)SuccinctEdgeProceedings of the VLDB Endowment10.14778/3415478.341549313:12(2857-2860)Online publication date: 1-Aug-2020
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Huang JAbadi DRen K(2020)Scalable SPARQL querying of large RDF graphsProceedings of the VLDB Endowment10.14778/3402707.34027474:11(1123-1134)Online publication date: 3-Jun-2020
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