DRIMS: A Software Tool to Incrementally Maintain Previous Discovered Rules

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10333)


A wide spectrum of methods for knowledge extraction have been proposed up to date. These expensive algorithms become inexact when new transactions are made into business data, an usual problem in real-world applications. The incremental maintenance methods arise to avoid reruns of those algorithms from scratch by reusing information that is systematically maintained. This paper introduces a software tool: Data Rules Incremental Maintenance System (DRIMS) which is a free tool written in Java for incrementally maintain three types of rules: association rules, approximate dependencies and fuzzy association rules. Several algorithms have been implemented in this tool for relational databases using their active resources. These algorithms are inspired in efficient computation of changes and do not include any mining technique. We operate on discovered rules in their final form and sustain measures of rules up-to-date, ready for real-time decision support. Algorithms are applied over a generic form of measures allowing the maintenance of a wide rules’ metrics in an efficient way. DRIMS software tool do not discover new knowledge, it has been designed to efficiently maintain interesting information previously extracted.


Association rules Approximate dependencies Incremental maintenance Active databases 



This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund - ERDF (Fondo Europeo de Desarrollo Regional - FEDER) under project TIN2014-58227-P Descripción lingüística de información visual mediante técnicas de minería de datos y computación flexible.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University “Marta Abreu” of Las VillasSanta ClaraCuba
  2. 2.University of GranadaGranadaSpain
  3. 3.University of JaénJaénSpain

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