Towards Scalable Non-Monotonic Stream Reasoning via Input Dependency Analysis

Abstract

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The high expressiveness of non-monotonic reasoning enables complex decision making by managing defaults, commonsense, preferences, recursion, and non-determinism, but it is computationally intensive. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art non-monotonic reasoners to be applied to streaming information in a scalable way. In this paper, we address the issue of scalability for nonmonotonic stream reasoning based on Answer Set Programming (ASP) - an expressive reasoning approach based on disjunctive logic Datalog with negation under the stable model semantics, by analyzing input dependency. We introduce an input dependency graph to represent the relationships between input events based on the structure of a given logical rule set. The input dependency graph allows us to dynamically configure the streaming window size in order to maximise the scalability of the non-monotonic reasoner. We conduct an experimental evaluation to demonstrate the effectiveness and ability of our proposed approach in improving the scalability of disjunctive logic programming with ASP in dynamic environments.