A FRAMEWORK FOR KNOWLEDGE DISCOVERY AND EVOLUTION IN DATABASES

doi:10.1109/69.250080

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A FRAMEWORK FOR KNOWLEDGE DISCOVERY AND EVOLUTION IN DATABASES

1993-01-01

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Larry Kerschberg

DOI：10.1109/69.250080

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Abstract

En 中文

Although knowledge discovery is increasingly important in databases, discovered knowledge is not always useful to users. It is mainly because the discovered knowledge does not fit the user's interests, or it may be redundant or inconsistent with a priori knowledge. Knowledge discovery in databases depends critically on how well a database is characterized and how consistently the existing and discovered knowledge is evolved. This paper describes a novel concept for knowledge discovery and evolution in databases. The key issues of this work include: using a database query to discover new rules; using not only positive examples (answer to a query) but also negative examples to discover new rules; harmonizing existing rules with the new rules. The main contribution of this paper is the development of a new tool for 1) characterizing the exceptions in databases and 2) evolving knowledge as a database evolves.

Keywords:

ACTIVE DATABASE EVOLUTION

DATABASE MINING

EXPERTISE TRANSFER

KNOWLEDGE DISCOVERY

KNOWLEDGE REFINEMENT

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