JFUSE: Json FUll Schema Extractor
DOI:
https://doi.org/10.5753/jidm.2026.5748Keywords:
schema discovering, schema extraction, JSON, metamodel, tagged union, enumerationAbstract
Lately, we have witnessed a flood of data generated by several data-centric applications, and the generated data are available in a large fashion of formats.
However, those data are mostly weakly structured, irregular, and incomplete; they do not follow a predefined schema. A challenging task is to understand how those data are organized and structured. JSON has become a trendy format for data-centric applications to store and share data. Its success is due to embodying structure and data in the same representation.
This makes JSON documents loosely coupled with schemas. Still, schemas are essential for applications to deal with the data more efficiently. In this paper, we propose JFUSE, a tool to deal with the problem of discovering a schema from JSON collections. Besides inferring basic types (e.g., atomic types, arrays, and objects), JFUSE also discovers fields that represent keys in the collection, fields minimum/maximum constraint values, enumeration, tagged unions, metadata as data, objects as collections, and arrays as tuples. We propose a metamodel that can be easily transformed into any schema language (e.g., JSON Schema). Our experiments show that the proposed approach infers concise and correct schemas from (huge) JSON collections.basic types (e.g., atomic types, arrays, and objects), JFUSE also discovers fields that represent keys in the collection, fields minimum/maximum constraint values, enumeration, tagged unions, metadata as data, objects as collections, and arrays as tuples. We propose a metamodel that can be easily transformed into any schema language (e.g., JSON Schema). Our experiments show that the proposed approach infers concise and correct schemas from (huge) JSON collections.
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References
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