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Authordc.contributor.authorSalas, Jaime 
Authordc.contributor.authorHogan, Aidan 
Admission datedc.date.accessioned2019-05-31T15:21:10Z
Available datedc.date.available2019-05-31T15:21:10Z
Publication datedc.date.issued2018
Cita de ítemdc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volumen 11136 LNCS, 2018, Pages 600-616
Identifierdc.identifier.issn16113349
Identifierdc.identifier.issn03029743
Identifierdc.identifier.other10.1007/978-3-030-00671-6_35
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/169523
Abstractdc.description.abstractCaching in the context of expressive query languages such as SPARQL is complicated by the difficulty of detecting equivalent queries: deciding if two conjunctive queries are equivalent is NP-complete, where adding further query features makes the problem undecidable. Despite this complexity, in this paper we propose an algorithm that performs syntactic canonicalisation of SPARQL queries such that the answers for the canonicalised query will not change versus the original. We can guarantee that the canonicalisation of two queries within a core fragment of SPARQL (monotone queries with select, project, join and union) is equal if and only if the two queries are equivalent; we also support other SPARQL features but with a weaker soundness guarantee: that the (partially) canonicalised query is equivalent to the input query. Despite the fact that canonicalisation must be harder than the equivalence problem, we show the algorithm to be practical for real-world queries taken from SPARQL endpoint logs, and further show that it detects more equivalent queries than when compared with purely syntactic methods. We also present the results of experiments over synthetic queries designed to stress-test the canonicalisation method, highlighting difficult cases.
Lenguagedc.language.isoen
Publisherdc.publisherSpringer Verlag
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
Sourcedc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywordsdc.subjectTheoretical Computer Science
Keywordsdc.subjectComputer Science (all)
Títulodc.titleCanonicalisation of monotone SPARQL queries
Document typedc.typeArtículo de revista
Catalogueruchile.catalogadorjmm
Indexationuchile.indexArtículo de publicación SCOPUS
uchile.cosechauchile.cosechaSI


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile