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Ad-hoc data models like Json simplify schema evolution and enable multiplexing various data sources into a single stream. While useful when writing data, this flexibility makes Json harder to validate and query, forcing such tasks to rely on automated schema discovery techniques. Unfortunately, ambiguity in the schema design space forces existing schema discovery systems to make simplifying, data-independent assumptions about schema structure. When these assumptions are violated, most notably by APIs, the generated schemas are imprecise, creating numerous opportunities for false positives during validation. In this paper, we propose Jxplain, a Json schema discovery algorithm with heuristics that mitigate common forms of ambiguity. Although Jxplain is slightly slower than state of the art schema extractors, we show that it produces significantly more precise schemas. 

Ad-hoc data models like JSON make it easy to evolve schemas and to multiplex different data-types into a single stream. This flexibility makes JSON great for generating data, but also makes it much harder to query, ingest into a database, and index. In this paper, we explore the first step of JSON data loading: schema design. Specifically, we consider the challenge of designing schemas for existing JSON datasets as an interactive problem. We present SchemaDrill, a roll-up/drill-down style interface for exploring collections of JSON records. SchemaDrill helps users to visualize the collection, identify relevant fragments, and map it down into one or more flat, relational schemas. We describe and evaluate two key components of SchemaDrill: (1) A summary schema representation that significantly reduces the complexity of JSON schemas without a meaningful reduction in information content, and (2) A collection of schema visualizations that help users to qualitatively survey variability amongst different schemas in the collection.