Efficient Computation of the Well-Founded Semantics over Big Data
  Data originating from the Web, sensor readings and social media result in
increasingly huge datasets. The so called Big Data comes with new scientific
and technological challenges while creating new opportunities, hence the
increasing interest in academia and industry. Traditionally, logic programming
has focused on complex knowledge structures/programs, so the question arises
whether and how it can work in the face of Big Data. In this paper, we examine
how the well-founded semantics can process huge amounts of data through mass
parallelization. More specifically, we propose and evaluate a parallel approach
using the MapReduce framework. Our experimental results indicate that our
approach is scalable and that well-founded semantics can be applied to billions
of facts. To the best of our knowledge, this is the first work that addresses
large scale nonmonotonic reasoning without the restriction of stratification
for predicates of arbitrary arity. To appear in Theory and Practice of Logic
Programming (TPLP).
