  Recent technological advances have led to unprecedented amounts of generated
data that originate from the Web, sensor networks and social media. Analytics
in terms of defeasible reasoning - for example for decision making - could
provide richer knowledge of the underlying domain. Traditionally, defeasible
reasoning has focused on complex knowledge structures over small to medium
amounts of data, but recent research efforts have attempted to parallelize the
reasoning process over theories with large numbers of facts. Such work has
shown that traditional defeasible logics come with overheads that limit
scalability. In this work, we design a new logic for defeasible reasoning, thus
ensuring scalability by design. We establish several properties of the logic,
including its relation to existing defeasible logics. Our experimental results
indicate that our approach is indeed scalable and defeasible reasoning can be
applied to billions of facts.
