Temporal Data Analytics with Temporal Abstraction and TIRPs
Temporal Data Analytics with Temporal Abstraction and TIRPs
Prof. Robert Moskovitch
Head, Complex Data Analytics Lab Software and Information Systems Engineering Ben Gurion University of the Negev, Israel
Abstract:
Analysis of heterogeneous multivariate time-stamped data is one of the most challenging topics in data science in general, relevant to various problems in real-life longitudinal data in many domains, such as cybersecurity, healthcare, predictive maintenance, sports, and more. Timestamped data can be sampled regularly, commonly by electronic means, but also irregularly, often made manually - common in biomedical data, whether intense as in ICU or sparse as in Electronic Health Records (EHR). Additionally, raw temporal data can represent durations of a continuous or nominal value represented by time intervals. Transforming time point series into meaningful symbolic time intervals, using Temporal Abstraction, to bring all the temporal variables, having various representations, into a uniform representation will be presented. Then, KarmaLego (IEEE ICDM 2015), or TIRPClo (AAAI 2021, DMKD 2023), fast time intervals mining algorithms for the discovery of non-ambiguous Time Intervals Related Patterns (TIRPs) represented by Allen's temporal relations, will be introduced. TIRPs can be used for several purposes: temporal knowledge discovery or as features for the classification of heterogeneous multivariate temporal data (KAIS 2015), and with increased accuracy when using the Temporal Discretization for Classification (TD4C) method (DMKD 2015). In this talk, I will refer to our recent developments and publications in faster TIRPs mining, visualization of TIRPs discovery (JBI 2022), and the very recent novel use of TIRPs for event’s continuous prediction (SDM 2024, ML 2025) based on the continuous prediction of a pattern’s completion, and more
Bio:
Prof. Robert Moskovitch is the head of the Complex Data Analytics Lab and a member of the faculty of Computer and Information Science (former Software and Information Systems Engineering) at Ben Gurion University, Israel. He is also an adjunct faculty member at the Population Health Science and Policy department at Icahn Medical School at Mount Sinai, NYC, USA. Before his postdoc fellowship at the Department of Biomedical Informatics at Columbia University in NYC, he headed several R&D projects in Information Security at the Deutsche Telekom Innovation Laboratories. He is an Associate Editor at ACM Transactions on Knowledge from Data (TKDD) and other journals (Big Data, PLOS ONE), as well as on the editorial board of the Journal of Biomedical Informatics (JBI) and others. He is the elected Vice Chair of the Board of the Artificial Intelligence in Medicine (AIME) Society and was the general co-chair of the international conference on Artificial Intelligence in Medicine (AIME) 2024. He serves on program committees of conferences, such as Area Chair at ACM KDD Research Track, IJCAI, AAAI, AIME, and more, as well as workshops in Biomedical Informatics and Information Security. He co-edited special issues at JASIST, JBI, JAIR and AIMJ. He published more than a hundred peer-reviewed papers in leading journals and conferences, such as ACM TKDD, Data Mining and Knowledge Discovery, Machine Learning, JAIR, Information Sciences, KAIS, JAMIA, JBI, AAAI, IEEE ICDM, SDM, AMIA, AIME and more. His lab focuses mainly on Temporal Data Analytics and its use and applications to the biomedical, security, sport, and other domains, but not exclusively.
