Article

FedX: optimization techniques for federated query processing on linked data

Authors:

Andreas Schwarte,

Michael SchmidtAuthors Info & Claims

ISWC'11: Proceedings of the 10th international conference on The semantic web - Volume Part I

Pages 601 - 616

Published: 23 October 2011 Publication History

Abstract

Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on theWeb, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.

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Published In

cover image Guide Proceedings

ISWC'11: Proceedings of the 10th international conference on The semantic web - Volume Part I

October 2011

809 pages

ISBN:9783642250729

Editors:
Lora Aroyo
VU University Amsterdam, The Netherlands
,
Chris Welty
IBM Research,Yorktown Heights, NY
,
Harith Alani
The Open University, Milton Keynes, UK
,
Jamie Taylor
Google, Mountain View, CA
,
Abraham Bernstein
University of Zurich, Switzerland

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Yahoo! Research
Microsoft Research: Microsoft Research
OASIS: OASIS
THESEUS: THESEUS

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 October 2011

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https://dl.acm.org/doi/10.1145/3659677.3659836
Yuan YMa DWen ZZhang ZWang G(2021)Subgraph matching over graph federationProceedings of the VLDB Endowment10.14778/3494124.349412915:3(437-450)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.14778/3494124.3494129
Cheng SHartig O(2020)FedQPLProceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services10.1145/3428757.3429120(436-445)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428757.3429120
Mountantonakis MTzitzikas Y(2019)Large-scale Semantic Integration of Linked DataACM Computing Surveys10.1145/334555152:5(1-40)Online publication date: 13-Sep-2019
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Minier TSkaf-Molli HMolli P(2019)SaGe: Web Preemption for Public SPARQL Query ServicesThe World Wide Web Conference10.1145/3308558.3313652(1268-1278)Online publication date: 13-May-2019
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Halvorsen JStolpe A(2018)Towards practical federation of BGPs in the presence of blank nodesProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227644(1-12)Online publication date: 25-Jun-2018
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Siow ETiropanis THall W(2018)Analytics for the Internet of ThingsACM Computing Surveys10.1145/320494751:4(1-36)Online publication date: 25-Jul-2018
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