Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Machine Learning - special issue on inductive logic programming
A relational model of data for large shared data banks
Communications of the ACM
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
Discovery of relational association rules
Relational Data Mining
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
Knowledge Discovery in databases - An Inductive Logic Programming Approach
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Discovery of First-Order Regularities in a Relational Database Using Offline Candidate Determination
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Relational learning as search in a critical region
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Generic cut actions for external prolog predicates
PADL'06 Proceedings of the 8th international conference on Practical Aspects of Declarative Languages
On applying tabling to inductive logic programming
ECML'05 Proceedings of the 16th European conference on Machine Learning
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Hi-index | 0.00 |
A consequence of ILP systems being implemented in Prolog or using Prolog libraries is that, usually, these systems use a Prolog internal database to store and manipulate data. However, in real-world problems, the original data is rarely in Prolog format. In fact, the data is often kept in Relational Database Management Systems (RDBMS) and then converted to a format acceptable by the ILP system. Therefore, a more interesting approach is to link the ILP system to the RDBMS and manipulate the data without converting it. This scheme has the advantage of being more scalable since the whole data does not need to be loaded into memory by the ILP system. In this paper we study several approaches of coupling ILP systems with RDBMS systems and evaluate their impact on performance. We propose to use a Deductive Database (DDB) system to transparently translate the hypotheses to relational algebra expressions. The empirical evaluation performed shows that the execution time of ILP algorithms can be effectively reduced using a DDB and that the size of the problems can be increased due to a non-memory storage of the data.