Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems

Abstract

Data Mining (DM) algorithms are able to construct models from available data that can be very useful for both business and science. However, a powerful representation language is required to express the highly complex models that stem from structured data. Multirelational algorithms can then take advantage of this representation for both data and models. The drawback is that for very large or highly complex domains multirelational algorithms may require long running times. This problem can be substantially reduced using parallel implementations. In this chapter, we present a survey on parallel approaches to run Inductive Logic Programming (ILP), a flavor of multirelational algorithms. We also analyze different scheduling approaches for those implementations and describe two applications where the proposed approaches may be very useful.