Improving the scalability of knowledge discovery in databases systems.

摘要

The explosive growth of databases in different scientific, industrial, commercial, medical and other fields has not been accompanied by a similar growth in our ability to analyze and digest this data, creating an urgent need for automatic database analysis tools. These tools constitute the building blocks for knowledge discovery in databases (KDD). By definition, such systems are intended to deal with huge databases which overwhelm researchers' abilities to analyze them. KDD systems that can scale with the growth of databases are one of the most desirable end-products of computing; however, they are also one of the most difficult tasks in computing.;This research outlines a general approach for scaling KDD systems and applies the suggested strategies to the SUBDUE knowledge discovery system. Potential achievements and obstacles are discussed.