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<p>Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research.</p>
knowledge discovery; real-world databases; idealized knowledge-discovery system; CoverStory; EXPLORA; Knowledge Discovery Workbench; future research; KDD systems; machine learning; knowledge acquisition; deductive databases; knowledge acquisition; knowledge based systems; learning (artificial intelligence)

P. Chan, G. Piatetsky-Shapiro and C. Matheus, "Systems for Knowledge Discovery in Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 903-913, 1993.
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