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A Type System for Interactive JSON Schema Inference (Extended Abstract)

Baazizi, Mohamed-Amine; Colazzo, Dario; Ghelli, Giorgio; Sartiani, Carlo (2019), A Type System for Interactive JSON Schema Inference (Extended Abstract), in Baier, Christel; Chatzigiannakis, Ioannis; Flocchini, Paola; Leonardi, Stefano, 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019), Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, p. 101:1--101:13. 10.4230/LIPIcs.ICALP.2019.101

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LIPIcs-ICALP-2019-101.pdf (577.9Kb)
Type
Communication / Conférence
Date
2019
Conference title
46th International Colloquium on Automata, Languages, and Programming (ICALP 2019)
Conference date
2019-07
Conference city
Patras
Conference country
Greece
Book title
46th International Colloquium on Automata, Languages, and Programming (ICALP 2019)
Book author
Baier, Christel; Chatzigiannakis, Ioannis; Flocchini, Paola; Leonardi, Stefano
Publisher
Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
ISBN
978-3-95977-109-2
Pages
101:1--101:13
Publication identifier
10.4230/LIPIcs.ICALP.2019.101
Metadata
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Author(s)
Baazizi, Mohamed-Amine
Laboratoire d'Informatique de Paris 6 [LIP6]
Colazzo, Dario
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ghelli, Giorgio
Dipartimento di Informatica [Pisa]
Sartiani, Carlo
Dipartimento di Matematica Informatica ed Economia [DiMIE]
Abstract (EN)
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.
Subjects / Keywords
JSON; type systems; interactive inference

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