[Archimedes Talks Series]Advanced Data Mining and Search Techniques for Natural Spatio- Temporal Systems - Dr. Matthias Renz

Title: Advanced Data Mining and Search Techniques for Natural Spatio-Temporal Systems

Speaker: Professor Matthias Renz (Christian-Albrechts-Universität zu Kiel (CAU, University of Kiel))

Abstract: The study of natural spatio-temporal systems—such as climate patterns, environmental phenomena, and dynamic processes in the ocean—presents unique challenges due to the complexity and dynamic nature of data. Advanced data mining and search techniques play a critical role in analyzing and interpreting these systems, enabling the extraction of meaningful patterns and knowledge from large, complex datasets. In this talk, I will highlight some of our recent studies in data mining and query processing we have conducted in a number of interdisciplinary collaborative projects. This includes water-mass connectivity pattern mining, spatio-temporal correlation clustering and predictive modeling, tailored to address the complexities of spatio-temporal data to understand complex systems in ocean dynamics. We also delve into the development of search algorithms for CO2 minimizing ship routing in large-scale, dynamic, multi-attribute ocean data, focusing on optimizing performance and scalability. This talk underpins the importance of interdisciplinary strategies in advancing the field and fostering deeper insights into the behavior of natural systems over time and space.

Short bio: Dr. Matthias Renz is Professor at the Department of Computer Science at Christian-Albrechts-Universität zu Kiel (CAU, University of Kiel). Before he joined CAU in summer 2018, Dr. Renz was Associate Professor in the Computational and Data Science Department at George Mason University (GMU), Fairfax, VA, USA. He received his Ph.D. in Computer Science at Ludwig-Maximilians-Universität München in 2006, where he served as lecturer after finishing his habilitation (venia legendi) 2011. Dr. Renz’s main research interest is Data Science with focus on scalable methods for searching and mining in very large, heterogeneous, dynamic and potentially uncertain data. He is substantially involved in a number of interdisciplinary collaborative research projects including the Gaia-X initiative Marispace-X, NFDI4Objects, MarData Helmholtz Information & Data Science Academy, Data Campus, ArcWorlds among others and is head of the CAU Center for Interdisciplinary Data Science. He gave several invited tutorials, seminars, and keynotes on international conferences and in 2016 his work received the 10-Year Best Paper Award at the International Conference on Database Systems for Advanced Applications. He has been program committee member for several international conferences and workshops, including SIGMOD, VLDB, ICDE, and KDD. Furthermore, Dr. Renz co-organized and chaired several international conferences and workshops, including SSTD, ACM SIGSPATIAL, DASFAA, ACM SIGSPATIAL QUeST, ACM SIGMOD GeoRich, and ACM SIGSPATIAL LocalRec.

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