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dc.contributor.authorGaignard, Alban
dc.contributor.authorBelhajjame, Khalid
dc.contributor.authorSkaf-Molli, Hala
dc.date.accessioned2020-05-25T10:25:24Z
dc.date.available2020-05-25T10:25:24Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20773
dc.descriptionLecture Notes in Computer Science book series (LNCS, volume 10577)en
dc.language.isoenen
dc.subjectReproducibilityen
dc.subjectScientific Workflowsen
dc.subjectProvenanceen
dc.subjectProv Constraintsen
dc.subject.ddc004en
dc.titleSHARP: Harmonizing and Bridging Cross-Workflow Provenanceen
dc.typeCommunication / Conférence
dc.description.abstractenPROV has been adopted by a number of workflow systems for encoding the traces of workflow executions. Exploiting these provenance traces is hampered by two main impediments. Firstly, workflow systems extend PROV differently to cater for system-specific constructs. The difference between the adopted PROV extensions yields heterogeneity in the generated provenance traces. This heterogeneity diminishes the value of such traces, e.g. when combining and querying provenance traces of different workflow systems. Secondly, the provenance recorded by workflow systems tends to be large, and as such difficult to browse and understand by a human user. In this paper (extending, initially published at SeWeBMeDA’17), we propose SHARP, a Linked Data approach for harmonizing cross-workflow provenance. The harmonization is performed by chasing tuple-generating and equality-generating dependencies defined for workflow provenance. This results in a provenance graph that can be summarized using domain-specific vocabularies. We experimentally evaluate SHARP (i) on publicly available provenance documents and (ii) using a real-world omic experiment involving workflow traces generated by the Taverna and Galaxy systems.en
dc.identifier.citationpages219-234en
dc.relation.ispartofeditorBlomqvist, Eva
dc.relation.ispartofeditorHose, Katja
dc.relation.ispartofeditorPaulheim, Heiko
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityChamen
dc.relation.ispartofpages387en
dc.relation.ispartofurl10.1007/978-3-319-70407-4en
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01768385en
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-3-319-70407-4en
dc.relation.conftitleThe Semantic Web: ESWC 2017 Satellite Eventsen
dc.relation.confdate2017-05
dc.relation.confcityPortorožen
dc.relation.confcountrySloveniaen
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-319-70407-4_35en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2020-05-25T10:21:33Z
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