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Collaborative SPARQL Query Processing for Decentralized Semantic Data

Abstract : Decentralization allows users to regain freedom and control over their digital life. As a global shared data space, the Linked Data already supports decentralization. Data providers are free to publish their data on their web domains and users can execute decentralized SPARQL queries over multiple data sources. However, decentralization makes query processing challenging, raising well-known problems of source discovery, answer completeness and performance. Existing approaches for decentralized SPARQL query processing raise issues related to autonomy and answer completeness. In this paper, we propose Qasino, an original approach for querying decentralized RDF data that targets both answer completeness, and source autonomy. Qasino is based on a decentralized random service that allows for discovering all relevant data sources. To speed up query processing, sources executing similar queries cooperate by sharing their intermediate results. Our experimental results demonstrate that collaborative query processing can significantly speedup query processing in a decentralized setup.
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Contributor : Pascal Molli Connect in order to contact the contributor
Submitted on : Sunday, February 28, 2021 - 5:23:46 PM
Last modification on : Wednesday, April 27, 2022 - 3:43:47 AM
Long-term archiving on: : Saturday, May 29, 2021 - 6:18:02 PM


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Arnaud Grall, Hala Skaf-Molli, Pascal Molli, Matthieu Perrin. Collaborative SPARQL Query Processing for Decentralized Semantic Data. Database and Expert Systems Applications - 31st International Conference, 2020, Bratislava, Slovakia, September 14-17, 2020, Proceedings, Part , pp.320-335, 2020, ⟨10.1007/978-3-030-59003-1_21⟩. ⟨hal-03154375⟩



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