Panagiota Fatourou

Panagiota Fatourou

Professor at the Department of Computer Science of the University of Crete, Greece

Panagiota Fatourou is a Professor at the Department of Computer Science of the University of Crete and the Institute of Computer Science (ICS) of the Foundation for Research and Technology (FORTH). She has worked as a visiting Professor at the School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne in Switzerland, and as a visiting researcher at the University of York and the University of Toronto in Canada. She has been a Marie-Curie Individual Fellow at Université Paris Cité, and a postdoc at Max-Planck Institut für Informatik, Saarbrücken, Germany, and at the Computer Science Department of the University of Toronto. Her research interests focus on parallel and distributed computing.P. Fatourou has served as the chair of the ACM Europe Council (October 2019 – June 2021). Since July 2015, she is an elected member of the Council, currently serving as the Past Chair.  She has served as the editor of the Distributed Computing Column of the Bulletin of the European Association for Theoretical Computer Science, and as the General Chair of the ACM Symposium on Principles of Distributed Computing(PODC 2013). She has also served as a member-at-large of the steering committees of PODC and OPODIS. She has been the PC co-chair of the 20^th  International Conference on Principles of Distributed Systems (OPODIS 2016), and of the 19th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS 2017). She has also been an ACM Distinguished Speaker and a Featured ACM Member.

Abstract: Parallel and Distributed Data Series Processing

Processing of large collections of real-world data series is nowadays one of the most challenging and critical problems for a wide range of diverse application domains, including finance, seismology and other earth sciences, astrophysics, neuroscience, engineering, etc.  Due to the unprecedented growth in size that data series collections experience nowadays, traditional, serial-execution data series indexing technologies are rendered inadequate. Thus, one of the most pressing issues in data series processing is achieving enhanced performance and high scalability. This tutorial focuses on major techniques for building distributed and concurrent data series indexing solutions, which are designed to inherently take advantage of modern hardware, in order to accelerate data series processing times for both on-disk and in-memory data. In particular, we will study a collection of data series indices that utilize the entire computational power of modern clusters (multiple nodes, multi-core and SIMD architecture of each node, as well as Graphics Processing Units (GPUs)) to tackle the performance and scalability goals in data series processing.

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