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Among data-intensive applications that are beyond the reach of traditional Data Base Management Systems (DBMS), data mining stands out because of practical importance and the complexity of the research problems that must be solved before the vision of Inductive DBMS can become a reality. In this paper, the authors first discuss technical developments that have occurred since the very notion of Inductive DBMS emerged as a result of the seminal papers authored by Imielinski and Mannila a decade ago. The research progress achieved since then can be subdivided into three main problem subareas as follows: Language, optimization, and representation.
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