Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
ECML '93 Proceedings of the European Conference on Machine Learning
Data Mining the Yeast Genome in a Lazy Functional Language
PADL '03 Proceedings of the 5th International Symposium on Practical Aspects of Declarative Languages
Parallel Execution for Speeding Up Inductive Logic Programming Systems
DS '99 Proceedings of the Second International Conference on Discovery Science
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Concurrent Execution of Optimal Hypothesis Search for Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
MPI: A Message-Passing Interface Standard
MPI: A Message-Passing Interface Standard
Accelerating the Drug Design Process through Parallel Inductive Logic Programming Data Mining
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
An empirical study of the use of relevance information in inductive logic programming
The Journal of Machine Learning Research
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
ROCCER: an algorithm for rule learning based on ROC analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Pattern Recognition as Rule-Guided Inductive Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Scalable Induction of Logic Programs Using a Deductive Database System
Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Distributed generative data mining
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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It is well known by Inductive Logic Programming (ILP) practioners that ILP systems usually take a long time to find valuable models (theories). The problem is specially critical for large datasets, preventing ILP systems to scale up to larger applications. One approach to reduce the execution time has been the parallelization of ILP systems. In this paper we overview the state-of-the-art on parallel ILP implementations and present work on the evaluation of some major parallelization strategies for ILP. Conclusions about the applicability of each strategy are presented.