conclusion.tex

\section{CONCLUSION}
\label{sec:concl}
\par In this paper, we have presented a system that provides  a distributed pattern prediction over multiple large-scale event streams of moving objects (vessels). The system uses the event forecasting with Pattern Markov Chain (PMC) \cite{alevizos2017event} as the base prediction model on each event stream, and it applies the protocol for distributed online prediction \cite{kamp2014communication} to exchange information between the prediction models over multiple input event streams. Our proposed system has been implemented using Apache Flink and Apache Kafka. In order to show the usefulness and effectiveness of our approach, we empirically tested it against large real-world event streams related to trajectories of moving vessels.

\par As future work, we will address the open issues emerging from the current findings. Firstly,  we will study the interrelation between precision and spread scores by validating the approach over synthetic event streams. Secondly, we will investigate the effect of grouping the input event streams on the predictive performance of our method.