Efficient and Scalable Induction of Logic Programs using a Deductive Database System

Michel Ferreira, Ricardo Rocha, Tiago Soares and Nuno A. Fonseca

August 2006


In this work we present an user transparent approach to couple an ILP system with a relational database by using a Deductive Database (DDB) system. Our proposal uses the DDB engine to transparently translate the generated hypotheses to SQL statements with only minor changes to the implementation of the ILP system. By transferring as much as possible the evaluation of hypotheses to the RDBMS, we show how this coupled environment provides an excellent framework for the efficient and scalable execution of ILP algorithms. Being able to abstract the Prolog to SQL translation by using the DDB, we concentrate on evaluating several high-level schemes of interaction between the ILP system and the RDBMS, with different distributions of work between the logic system and the database system. Our results indicate that the execution time of ILP algorithms can be effectively reduced and that the size of the problems solved can be significantly increased due to a non-memory storage of the data-sets. Best performance is achieved when we use a scheme that transforms the hypotheses into an equivalent SQL count query.


  author =    {M. Ferreira and R. Rocha and T. Soares and Nuno A. Fonseca},
  title =     {{Efficient and Scalable Induction of Logic Programs using a Deductive Database System}},
  booktitle = {Proceedings of the 16th International Conference on Inductive Logic 
               Programming (ILP 2006) - Short Papers},
  pages =     {74--76},
  editor =    {S. Muggleton and R. Otero},
  month =     {August},
  year =      {2006},
  address =   {Santiago de Compostela, Spain},
  publisher = {University of Corunna},

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