• Corpus ID: 233476500

Not Elimination and Witness Generation for JSON Schema

@article{Baazizi2021NotEA,
  title={Not Elimination and Witness Generation for JSON Schema},
  author={Mohamed Amine Baazizi and Dario Colazzo and Giorgio Ghelli and Carlo Sartiani and Stefanie Scherzinger},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.14828}
}
JSON Schema is an evolving standard for the description of families of JSON documents. JSON Schema is a logical language, based on a set of assertions that describe features of the JSON value under analysis and on logical or structural combinators for these assertions. As for any logical language, problems like satisfaction, not-elimination, schema satisfiability, schema inclusion and equivalence, as well as witness generation, have both theoretical and practical interest. While satisfaction is… 

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References

SHOWING 1-10 OF 10 REFERENCES

Type Safety with JSON Subschema

This paper presents a complementary technique: JSON subschema checking, which can be used for static type checking with JSON Schema, and applies an implementation of the algorithm to 8,548 pairs of real-world JSON schemas from different domains, demonstrating that it can decide the subscheMA question for most schema pairs and is always correct for schema pairs that it could decide.

JSON: Data model, Query languages and Schema specification

A formal data model for JSON documents is proposed and, based on the common features present in available systems using JSON, a lightweight query language is defined allowing us to navigate through JSON documents.

Jsongen: a quickcheck based library for testing JSON web services

A systematic approach to testing behavioural aspects of Web Services that communicate using the JSON data format by developing a finite state machine model for the testing of a JSON-based web service.

Extended Regular Expressions: Succinctness and Decidability

It is demonstrated that extended regular-expressions cannot be minimized effectively (neither with respect to length, nor number of variables), and that the tradeoff in size between extended and “classical” regular expressions is not bounded by any recursive function.

JSON Schema Validation: A Vocabulary for Structural Validation of JSON

This document specifies a vocabulary for JSON Schema to describe the meaning of JSON documents, provide hints for user interfaces working with JSON data, and to make assertions about what a valid document must look like.

Internet Engineering Task Force

An extremely efficient, elegant way to name arbitrary sized inter-meshed aggregations of multicast addresses in such a way that it is easy to calculate how to change the name to encompass many more related names.

Type safety with json subschema, 2019

  • 2019

Not elimination and witness generation for json schema

  • 2020

Json schema

  • 2020

Martín Ugarte, and Domagoj Vrgoč. Foundations of json schema

  • WWW '16
  • 2016