Expression and Processing of Inductive Queries

Expression and Processing of Inductive Queries

Edgard Benítez-Guerrero (Laboratorio Nacional de Informática Avanzada, Mexico) and Omar Nieva-García (Universidad del Istmo, Mexico)
DOI: 10.4018/978-1-60566-242-8.ch055
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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.
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Background

The Traditional Framework for DB Querying

Research on query languages and associated evaluation techniques has a long tradition in the database area. Several query languages such as SQL, OQL, and XQUERY have been proposed. They enable the user to retrieve data from a database and filter these data according to specific selection criteria. To evaluate a query Q, the traditional process is as follows. First, Q is syntactically and semantically analyzed to check its syntax and verify if the schema elements referenced in Q exist in the database schema. Second, Q is translated into an expression in a query algebra represented as a query tree QT. Third, QT is optimized using heuristics and cost-based functions to devise an execution plan P with the minimal cost. Finally, P is executed to get the final results.

Key Terms in this Chapter

Decision Rule: A rule of the form ‘if then ’, where is a Boolean combination of attribute tests and is the class assigned to an instance satisfying the conditions.

Inductive Databases: Databases that besides raw data contain inductive generalizations about that data.

Knowledge Discovery in Databases: The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.

Prediction Join: An operator for applying a set of decision rules to classify uncategorized data.

Classif ication Model: A set of rules used to predict the class of an instance based on its attribute values.

Object-Relational Databases: The extension of relational databases to include object-oriented concepts such as collections, ADTs, tuple references, and inheritance.

Data Mining: The application of algorithms for discovering patterns in data.

Pattern: An expression in some language representing a high-level description of a dataset.

Inductive Query Language: A query language to perform various operations on data such as data preprocessing, pattern discovery, and pattern postprocessing.

Abstract Data Type (ADT): Specification of a set of data and the set of operations that can be performed on the data.

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