Building Intelligent Learning Database Systems

Authors

  • Xindong Wu

DOI:

https://doi.org/10.1609/aimag.v21i3.1524

Abstract

Induction and deduction are two opposite operations in data-mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data. An intelligent learning database (ILDB) system integrates machine-learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based sys-tems) 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 systems and discusses practical issues for ILDB applications, such as instance selection and structured induction.

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Published

2000-09-15

How to Cite

Wu, X. (2000). Building Intelligent Learning Database Systems. AI Magazine, 21(3), 61. https://doi.org/10.1609/aimag.v21i3.1524

Issue

Section

Articles