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Title: Reducing Ambiguity in Json Schema Discovery
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.
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Award ID(s):
1750460 1640864
Publication Date:
Journal Name:
SIGMOD '21: International Conference on Management of Data
Page Range or eLocation-ID:
1732 to 1744
Sponsoring Org:
National Science Foundation
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