A Delta Debugger for ILP Query Execution

Because query execution is the most crucial part of Inductive Logic Programming (ILP) algorithms, a lot of effort is invested in developing faster execution mechanisms. These execution mechanisms typically have a low-level implementation, making them hard to debug. Moreover, other factors such as the complexity of the problems handled by ILP algorithms and size of the code base of ILP data mining systems make debugging at this level a very difficult job. In this work, we present the trace-based debugging approach currently used in the development of new execution mechanisms in hipP, the engine underlying the ACE Data Mining system. This debugger uses the delta debugging algorithm to automatically reduce the total time needed to expose bugs in ILP execution, thus making manual debugging step much lighter.Comment: Paper presented at the 16th Workshop on Logic-based Methods in Programming Environments (WLPE2006

Query Optimization: Combining Query Packs and the Once-Transformation

In Inductive Logic Programming (ILP), several techniques have been introduced to improve the eciency of query execution. One such technique is query pack execution. A set of queries with a common pre x, as it is generated by the re nement operator of a typical ILP system, can be executed faster after it is converted into a tree structure called a query pack. Query transformations, on the other hand, improve the eciency of executing a single query by transforming it into a dierent form that is more ecient to execute. Combining query packs with query transformations is dicult because a transformation may have a negative eect on the structure of the pack. The once-transformation is one of the most important query transformations and can improve the eciency of query execution by several orders of magnitude. In this work, we extend query pack execution in such a way that it is able to handle queries produced by the once-transformation. We do this in the context of ilProlog, a high performance Prolog system with speci c extensions for supporting ILP systems. We evaluate our approach on both arti cial domains and real world ILP applications