Complex event recognition in the big data eraComplex event recognition in the big data era Δημοσίευση σε Συνέδριο Conference Publication 2018-04-232017enThe concept of event processing is established as a generic computational paradigm in various application fields, ranging from data processing in Web environments, over maritime and transport, to finance and medicine. Events report on state changes of a system and its environment. Complex Event Recognition (CER) in turn, refers to the identification of complex/composite events of interest, which are collections of simple events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, fraud in electronic marketplaces, cardiac arrhythmias, and epidemic spread. In each scenario, CER allows to make sense of Big event Data streams and react accordingly. The goal of this tutorial is to provide a step-by-step guide for realizing CER in the Big Data era. To do so, it elaborates on major challenges and describes algorithmic toolkits for optimized manipulation of event streams characterized by high volume, velocity and/or lack of veracity, placing emphasis on distributed CER over potentially heterogeneous (data variety) event sources. Finally, we highlight future research directions in the field. http://creativecommons.org/licenses/by/4.0/1996-199943rd International Conference on Very Large Data BasesProceedings of the VLDB Endowment Giatrakos Nikolaos Γιατρακος Νικολαος Artikis, Alexander Deligiannakis Antonios Δεληγιαννακης Αντωνιος Garofalakis Minos Γαροφαλακης Μινως Association for Computing Machinery Event processing Complex Event Recognition