SQL-Based KDD with Infobright’s RDBMS: Attributes, Reducts, Trees

  • Jakub Wróblewski
  • Sebastian Stawicki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


We present a framework for KDD process implemented using SQL procedures, consisting of constructing new attributes, finding rough set-based reducts and inducing decision trees. We focus particularly on attribute reduction, which is important especially for high-dimensional data sets. The main technical contribution of this paper is a complete framework for calculating short reducts using SQL queries on data stored in a relational form, without a need of any external tools generating or modifying their syntax. A case study of large real-world data is presented. The paper also recalls some other examples of SQL-based data mining implementations. The experimental results are based on the usage of Infobright’s analytic RDBMS, whose performance characteristics perfectly fit the requirements of presented algorithms.


KDD Rough sets Reducts Decision trees Feature extraction SQL High-dimensional data 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jakub Wróblewski
    • 1
  • Sebastian Stawicki
    • 2
  1. 1.Infobright Inc.WarsawPoland
  2. 2.Institute of MathematicsUniversity of WarsawWarsawPoland

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