The extension of relational databases to include object-oriented concepts such as collections, ADTs, tuple references, and inheritance.
Published in Chapter:
Expression and Processing of Inductive Queries
Edgard Benítez-Guerrero (Laboratorio Nacional de Informática Avanzada, Mexico) and Omar Nieva-García (Universidad del Istmo, Mexico)
Copyright: © 2009
|Pages: 9
DOI: 10.4018/978-1-60566-242-8.ch055
Abstract
The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.