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. Time-stamped 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 Absorption will be presented to bring all the temporal variables, which have various representations, into a uniform representation. 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, Cell/Patterns, 2025), 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.

Robert Moskovitch is the head of the Complex Data Analytics Lab as a member of the Faculty of Computer and Information Science at Ben Gurion University, Israel. He is also an adjunct faculty member at the Department of Population Health Science and Policy at Ichan 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 the ACM Transactions on Knowledge Discovery from Data (TKDD) journal, Big Data journal, and was Academic Editor at PLOS ONE, and he is on the editorial board of the Journal of Biomedical Informatics (JBI), and served on other journal editorial boards. 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, ICDM, AIME, and more, and workshops in Biomedical Informatics and Information Security. He co-edited special issues at JASIST, JBI, JAIR, and AIMJ. He published more than a hundred refereed papers in leading journals and conferences, such as IEEE ICDM, SDM, AAAI, DMKD, ACM TKDD, JAIR, Information Sciences, JAMIA, JBI, AMIA, and AIME. His lab focuses mainly on temporal data analytics and generally data mining and machine learning, as well as its use and applications in biomedical, security, sports, and other domains.