SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model

  • Jörg Wicker
  • Lothar Richter
  • Kristina Kessler
  • Stefan Kramer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5212)


In this demonstration, we will present the concepts and an implementation of an inductive database – as proposed by Imielinski and Mannila – in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.


Data Mining Query Language Pattern Mining Inductive Logic Programming Knowledge Discovery Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jörg Wicker
    • 1
  • Lothar Richter
    • 1
  • Kristina Kessler
    • 1
  • Stefan Kramer
    • 1
  1. 1.Institut für Informatik I12Technische Universität MünchenGarching b. MünchenGermany

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