May 24, 2024
The Institute of Informatics & Telecommunications hosted invited speaker Prof. Themis Palpanas, a distinguished University Professor of Computer Science at the Université Paris Cité (France) for a talk titled Unsupervised Subsequence Anomaly Detection in Large Sequences on Friday 24 May 2024.
The talk took place physically at the Lecture Room of the Institute of Informatics & Telecommunications, while participation was also possible via zoom.
Watch the video of the talk here:
About the talk: Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains, including applications related to security. In this work, we present unsupervised methods suitable for domain agnostic subsequence anomaly detection. We explore two possible way to represent the normal behaviour of a long data series that lead to fast and accurate identification of abnormal subsequences. These normal representations are either based on subsequences (using a data structure called the normal model), or on graphs, by taking advantage of graph properties to encode the normal transitions between neighbouring subsequences of a long series. The experimental results, on a large set of synthetic and real datasets, demonstrate that the proposed approaches correctly identify single and recurrent anomalies of various types, without any prior knowledge of the characteristics of these anomalies. Our approaches outperform by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster.
Short Bio: Themis Palpanas is an elected Senior Member of the French University Institute (IUF), a distinction that recognizes excellence across all academic disciplines, and Distinguished Professor of Computer Science at the Université Paris Cité (France), where he is Director of the Data Intelligence Institute of Paris (diiP), and Director of the Data Management group, diNo. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the University of California at Riverside, University of Trento, and at IBM T.J. Watson Research Center, and visited Microsoft Research, and the IBM Almaden Research Center.
His interests include problems related to data science (big data analytics and machine learning applications). He is the author of 14 patents. He is the recipient of 3 Best Paper awards, a Best Runner-up Demo award, and the IBM Shared University Research (SUR) Award. His service includes the VLDB Endowment Board of Trustees (2018-2023), Editor-in-Chief for PVLDB Journal (2024-2025) and BDR Journal (2016-2021), PC Chair for IEEE BigData 2023 and ICDE 2023 Industry and Applications Track, General Chair for VLDB 2013, Associate Editor for the TKDE Journal (2014-2020), and Research PC Vice Chair for ICDE 2020.