25 Nov 2022, 15:00 Athens time
Parsimonious Representation Learning in the Wild: Leveraging Sparsity and Low-Rank in Unknown Model Order Problems
Complex Event Recognition (CER) refers to the activity of detecting patterns in streams of continuously arriving “event” data over (geographically) distributed sources. CER is a key ingredient of many contemporary Big Data applications that require the processing of such event streams in order to obtain timely insights and implement reactive and proactive measures. Examples of such applications 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, error conditions in smart energy grids, violations of maritime regulations, cardiac arrhythmias and epidemic spread. In each application, CER allows to make sense of streaming data, react accordingly, and prepare for counter-measures. In this talk, we will present the formal methods for CER, as they have been developed in the artificial intelligence community. To illustrate the reviewed approaches, we will use the domain of maritime situational awareness.
About the speaker
Alexander Artikis is an Associate Professor at the University of Piraeus (GR), and a Research Associate at NCSR Demokritos, leading the complex event recognition group1. He holds a PhD from Imperial College London on Multi-Agent Systems, while his research interests lie in the area of Artificial Intelligence. He has published over 100 papers in related journals and conferences. Alexander has been developing complex event processing techniques in the context of several EU-funded Big Data projects, and he was the scientific coordinator in some of them. He has given tutorials on complex event processing in IJCAI, KR, VLDB and ECAI. In 2020, he co-organised the Dagstuhl seminar on the “Foundations of Composite Event Recognition