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oai:drops-oai.dagstuhl.de:10677 2024-03-06T09:46:26Z ddc:004 open_access

A Type System for Interactive JSON Schema Inference (Extended Abstract) Baazizi, Mohamed-Amine Colazzo, Dario Ghelli, Giorgio Sartiani, Carlo JSON type systems interactive inference In this paper we present the first JSON type system that provides the possibility of inferring a schema by adopting different levels of precision/succinctness for different parts of the dataset, under user control. This feature gives the data analyst the possibility to have detailed schemas for parts of the data of greater interest, while more succinct schema is provided for other parts, and the decision can be changed as many times as needed, in order to explore the schema in a gradual fashion, moving the focus to different parts of the collection, without the need of reprocessing data and by only performing type rewriting operations on the most precise schema. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Mohamed-Amine Baazizi and Dario Colazzo and Giorgio Ghelli and Carlo Sartiani 2019 Is Part Of LIPIcs, Volume 132, 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ICALP.2019.101 urn:nbn:de:0030-drops-106774 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2019.101 eng https://creativecommons.org/licenses/by/3.0/legalcode