Professor, Department of Computer Science, Aalborg University, Denmark.

Biography:

Christian S. Jensen is Professor of Computer Science at Aalborg University, Denmark. His research concerns data management and analytics, including machine learning, data mining, and query processing, with a focus on temporal and spatio- temporal data. Christian is an ACM and IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several awards, most recently the 2022 ACM SIGMOD Contributions Award and the 2019 IEEE TCDE Impact Award. He is on the board of Villum Fonden, a major funder of research in Denmark and is vice-chair of the Danish National Research Foundation. In Norway, he chairs the scientific advisory board (SAB) of the Norwegian Research Center for AI Innovation. He recently completed terms as president of the steering committee of the Swiss National Research Program on Big Data and as a member of the SAB of the Max Planck Institute for Informatics.

Multivariate Time Series Analytics – Challenges and Progress

Due to the ongoing, widespread deployment of connected sensing devices, increasingly massive volumes of time-series data are being produced. The amount of Internet-of-Things data, much of which is time series data, is projected to exceed 170 zettabytes in 2026, being produced by 20+ billion devices. This data captures the states of diverse processes at an unprecedented level of detail, in turn enabling us to better understand and improve those processes. When harnessed properly, time series data thus holds the potential to enable tremendous value creation across numerous sectors.

This talk first characterizes the setting of time series, offering insight into the complexity of this type of data and its settings. It then covers selected, key challenges that many existing time series analysis methods aim to address. Finally, it presents an ongoing benchmarking effort, aiming to accelerate progress in time series AI by enabling easy, comprehensive, and fair comparisons of existing and new time series analysis methods.