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dc.contributor.authorBaazizi, Mohamed-Amine
dc.contributor.authorBerti, Clément
dc.contributor.authorColazzo, Dario
dc.contributor.authorGhelli, Giorgio
dc.contributor.authorSartiani, Carlo
dc.date.accessioned2020-09-04T13:10:50Z
dc.date.available2020-09-04T13:10:50Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20970
dc.language.isoenen
dc.subjectJSON
dc.subject.ddc005.7en
dc.titleHuman-in-the-Loop Schema Inference for Massive JSON Datasets
dc.typeCommunication / Conférence
dc.description.abstractenJSON established itself as a popular data format for representing data whose structure is irregular or unknown a priori. JSON collections are usually massive and schema-less. Inferring a schema describing the structure of these collections is crucial for formulating meaningful queries and for adopting schema-based optimizations. In a recent work, we proposed a Map/Reduce schema inference approach that either infers a compact representation of the input collection or a precise description of every possible shape in the data. Since no level of precision is ideal, it is more appealing to give the analyst the freedom of choosing between different levels of precisions in an interactive fashion. In this paper we describe a schema inference system offering this important functionality.
dc.identifier.citationpages635-638
dc.relation.ispartoftitleEDBT 2020 - 23nd International Conference on Extending Database Technology
dc.relation.ispartofpublnameSchloss Dagstuhl--Leibniz-Zentrum fuer Informatik
dc.subject.ddclabelOrganisation des donnéesen
dc.relation.ispartofisbn978-3-89318-083-7
dc.relation.conftitle23rd International Conference on Extending Database Technology, EDBT 2020
dc.relation.confdate2020
dc.relation.confcountryDENMARK
dc.relation.forthcomingnonen
dc.identifier.doi10.5441/002/edbt.2020.82
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-12-17T09:33:37Z
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