Learning Meta Knowledge for Database Checking

Jeffrey C. Schlimmer

Building a large-scale system often involves creating a large knowledge store, and as these grow and are maintained by a number of individuals, errors are inevitable. Exploring databases as a specialization of knowledge stores, this paper studies the hypothesis that descriptive, learned models can be prescriptively used to find errors. To that end, it describes an implemented system called CARPER. Applying CARPER to a real-world database demonstrates the viability of the approach and establishes a baseline of performance for future research.

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