Distributed Online Learning of Event Definitions
  Logic-based event recognition systems infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
that learns event definitions in the form of Event Calculus theories, in a
single pass over a data stream. In this work we present a version of OLED that
allows for distributed, online learning. We evaluate our approach on a
benchmark activity recognition dataset and show that we can significantly
reduce training times, exchanging minimal information between processing nodes.
