Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks
  With the constant increase of available data in various domains, such as the
Internet of Things, Social Networks or Smart Cities, it has become fundamental
that agents are able to process and reason with such data in real time. Whereas
reasoning over time-annotated data with background knowledge may be
challenging, due to the volume and velocity in which such data is being
produced, such complex reasoning is necessary in scenarios where agents need to
discover potential problems and this cannot be done with simple stream
processing techniques. Stream Reasoners aim at bridging this gap between
reasoning and stream processing and LASER is such a stream reasoner designed to
analyse and perform complex reasoning over streams of data. It is based on
LARS, a rule-based logical language extending Answer Set Programming, and it
has shown better runtime results than other state-of-the-art stream reasoning
systems. Nevertheless, for high levels of data throughput even LASER may be
unable to compute answers in a timely fashion. In this paper, we study whether
Convolutional and Recurrent Neural Networks, which have shown to be
particularly well-suited for time series forecasting and classification, can be
trained to approximate reasoning with LASER, so that agents can benefit from
their high processing speed.
