MPI-based Parallelization for ILP-based Multi-relational Concept Discovery

Multi-relational concept discovery is a predictive learning task that aims to discover descriptions of a target concept in the light of past experiences. Parallelization has emerged as a solution to deal with efficiency and scalability issues relating to large search spaces in concept discovery systems. In this work, we describe a parallelization method for the ILP-based concept discovery system called CRIS. CRIS is modified in such a way that steps involving high query processing are reorganized in a data parallel way. To evaluate the performance of the resulting system, called P-CRIS, a set of experiments is conducted.