Our paper “Uncertainty-Aware Event Analytics over Distributed Settings” was accepted at DEBS 2019
Nikos Giatrakos, Alexander Artikis, Antonios Deligiannakis and Minos Garofalakis
In complex event processing (CEP), simple derived event (SDE) tuples are combined in pattern matching procedures to derive com-plex events (CEs) of interest. Big Data applications analyze event streams online and extract CEs to support decision making procedures. At massive scale, such applications operate over distributed networks of sites where efficient CEP requires reducing communication as much as possible. Besides, events often encompass various types of uncertainty assigned on event attribute values, occurrence or detection rules. Therefore, massively distributed Big event Data applications in a world of uncertain events call for communication-efficient, uncertainty-aware CEP solutions, which is the focus ofthis work. As a proof-of-concept, we show how we bridge the gap between two recent CEP prototypes which utilize IBM PROactive Technology ONline as their CEP engine and each extend it towards only one of the dimensions of distribution and uncertainty.