Keynote by Prof. Dr. Tilmann Rabl

Title: A Look at the Ecological Impact of Big Data Systems

Abstract

Physical limitations are rapidly bringing hardware efficiency improvements to a halt. At the same time, the AI boom is demanding enormous increases in compute capacity. In the search for marginal profit, industry has mostly given up on former goals of carbon neutrality in a global arms race on AI. In this pivotal moment, research is needed to clear up the clouds hiding the true economic, ecologic, and societal costs of current IT trends to open alternative paths for sustainable computing.

In this talk, we will present current IT trends from an ecological perspective. We will discuss different measures of efficiency of data systems and methods to improve it. Incorporating estimations on hardware and power production carbon intensity, we will estimate ecological impact of hardware and review implications on data system development.

About the speaker:

Tilmann Rabl is a Professor for Data Engineering Systems at the Digital Engineering Faculty of the University of Potsdam and the Hasso Plattner Institute. Tilmann received his doctoral degree at the University of Passau. Afterwards, he was a postdoctoral researcher at the University of Toronto and the Technical University of Berlin. His current research focuses on efficiency of database and ML systems, hardware efficient data processing, benchmarking, and sustainability.

Tilmann Rabl holds the chair for Data Engineering Systems at the Hasso Plattner Institute and is Professor at the Digital Engineering Faculty of the University of Potsdam. He is also cofounder and scientific director of the startup bankmark. Tilmann Rabl received his PhD at the University of Passau in 2011. He spent 4 years at the University of Toronto as a postdoc in the Middleware Systems Research Group (MSRG). From 2015 to 2019, he was senior researcher and visiting professor at the Database Systems and Information Management (DIMA) group at Technische Universität Berlin and Vice Director of the Intelligent Analytics for Massive Data (IAM) Group at the German Research Center for Artificial Intelligence (DFKI). He has conducted extensive research in Big Data analytics and processing, including several works focused on efficient computing and optimization. He has participated in numerous high-impact international venues, holding several awards including EDBT Best Experiments and Analysis Paper, EDBT, ICDE & SIGMOD Best paper awards as well as ICDE Outstanding Meta-Reviewer and SIGMOD Distinguised Reviewer among others.