Abstract

An intelligent learning data base (ILDB) system is an integrated learning system which implements automatic knowledge acquisition from data bases by providing formalisms for 1) translation of standard data base information into a form suitable for use by its induction engines. 2) using induction techniques to produce knowledge from data bases, and 3) interpreting the knowledge produced efficiently to solve users' problems. Although a lot of work on knowledge acquisition from data bases has been done, the requirements for building practical learning systems to learn from conventional data bases are still far away for existing systems to reach. A crucial requirement is more efficient learning algorithms as realistic data bases are usually fairly large. Based on KEshell. dBASE3 and the low-order polynomial induction algorithm HCV. this paper presents a knowledge engineering shell. KEsheH2. which implements the 3 phases of automatic knowledge acquisition from data bases in an integral way.

INTRODUCTION

Over the past twenty years data base research has evolved technologies that are now widely used in almost every computing and scientific field. However, many new advanced applications including computer-aided design (CAD), computer-aided manufacturing (CAM), computer-aided software engineering (CASE), image processing, and office automation (OA) have revealed that traditional data base management systems (DBMSs) are inadequate, especially on the following cases [Wu 90b]:

• Conventional data base technology has laid particular stress on dealing with large amounts of persistent and highly structured data efficiently and using transactions for concurrency control and recovery.

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