Article

Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources

Authors:

Mohamed Nadjib Mami,

Jens LehmannAuthors Info & Claims

The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part II

Pages 229 - 245

https://doi.org/10.1007/978-3-030-30796-7_15

Published: 26 October 2019 Publication History

Abstract

The last two decades witnessed a remarkable evolution in terms of data formats, modalities, and storage capabilities. Instead of having to adapt one’s application needs to the, earlier limited, available storage options, today there is a wide array of options to choose from to best meet an application’s needs. This has resulted in vast amounts of data available in a variety of forms and formats which, if interlinked and jointly queried, can generate valuable knowledge and insights. In this article, we describe Squerall: a framework that builds on the principles of Ontology-Based Data Access (OBDA) to enable the querying of disparate heterogeneous sources using a unique query language, SPARQL. In Squerall, original data is queried on-the-fly without prior data materialization or transformation. In particular, Squerall allows the aggregation and joining of large data in a distributed manner. Squerall supports out-of-the-box five data sources and moreover, it can be programmatically extended to cover more sources and incorporate new query engines. The framework provides user interfaces for the creation of necessary inputs, as well as guiding non-SPARQL experts to write SPARQL queries. Squerall is integrated into the popular SANSA stack and available as open-source software via GitHub and as a Docker image.

Software Framework. https://eis-bonn.github.io/Squerall.

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Van Assche DDelva THaesendonck GHeyvaert PDe Meester BDimou A(2023)Declarative RDF graph generation from heterogeneous (semi-)structured dataWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2022.10075375:COnline publication date: 1-Jan-2023
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Published In

cover image Guide Proceedings

The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part II

Oct 2019

582 pages

ISBN:978-3-030-30795-0

DOI:10.1007/978-3-030-30796-7

Editors:
Chiara Ghidini
Fondazione Bruno Kessler, Trento, Italy
,
Olaf Hartig
Linköping University, Linköping, Sweden
,
Maria Maleshkova
University of Bonn, Bonn, Germany
,
Vojtěch Svátek
University of Economics Prague, Prague, Czech Republic
,
Isabel Cruz
University of Illinois at Chicago, Chicago, IL, USA
,
Aidan Hogan
University of Chile, Santiago, Chile
,
Jie Song
Memect Technology, Beijing, China
,
Maxime Lefrançois
Mines Saint-Etienne, Saint-Etienne, France
,
Fabien Gandon
Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

© Springer Nature Switzerland AG 2019.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 October 2019

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Cited By

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https://dl.acm.org/doi/10.1145/3583780.3614753
Van Assche DDelva THaesendonck GHeyvaert PDe Meester BDimou A(2023)Declarative RDF graph generation from heterogeneous (semi-)structured dataWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2022.10075375:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.websem.2022.100753
Gu ZLanti DMosca AXiao GXiong JCalvanese D(2022)Ontology-based Data FederationProceedings of the 11th International Joint Conference on Knowledge Graphs10.1145/3579051.3579070(10-19)Online publication date: 27-Oct-2022
https://dl.acm.org/doi/10.1145/3579051.3579070
Bachir Belmehdi CKhiat AKeskes N(2022)OPTIMA: Framework Selecting Optimal Virtual Model to Query Large Heterogeneous DataBig Data Analytics and Knowledge Discovery10.1007/978-3-031-12670-3_18(209-215)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-12670-3_18
Van Assche D(2022)Balancing RDF Generation from Heterogeneous Data SourcesThe Semantic Web: ESWC 2022 Satellite Events10.1007/978-3-031-11609-4_40(264-274)Online publication date: 29-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-11609-4_40
Chaves-Fraga DRuckhaus EPriyatna FVidal MCorcho OSaleem MVerborgh RAli MHartig O(2021)Enhancing virtual ontology based access over tabular data with Morph-CSVSemantic Web10.3233/SW-21043212:6(869-902)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/SW-210432
Belcao MFalzone EBionda EValle E(2021)Chimera: A Bridge Between Big Data Analytics and Semantic TechnologiesThe Semantic Web – ISWC 202110.1007/978-3-030-88361-4_27(463-479)Online publication date: 24-Oct-2021
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Kalaycı EGrangel González ILösch FXiao Gul-Mehdi AKharlamov ECalvanese D(2020)Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge GraphsThe Semantic Web – ISWC 202010.1007/978-3-030-62466-8_29(464-481)Online publication date: 2-Nov-2020
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Jozashoori SChaves-Fraga DIglesias EVidal MCorcho O(2020)FunMap: Efficient Execution of Functional Mappings for Knowledge Graph CreationThe Semantic Web – ISWC 202010.1007/978-3-030-62419-4_16(276-293)Online publication date: 2-Nov-2020
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Mami MGrangel-González IGraux DElezi ELösch F(2020)Semantic Data Integration for the SMT Manufacturing Process Using SANSA StackThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_47(307-311)Online publication date: 31-May-2020
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