Building intelligent learning database systems

Induction and deduction are two opposite operations in data-mining applicat ions. Induction extracts knowledge in the form of, say, rules or decision t rees from existing data, and deduction applies induction results to interpr et new data. An intelligent learning database (ILDB) system integrates mach ine-learning techniques with database and knowledge base technology. It sta rts with existing database technology and performs both induction and deduc tion. The integration of database technology, induction (from machine learn ing), and deduction (from knowledge-based systems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This article presents a system structure for ILDB sy stems and discusses practical issues for ILDB applications, such as instanc e selection and structured induction.