Prof. Dr. Tilmann Rabl

About the Talk

Sensors, user input, and monitoring produce events at very high rates that are hard to process with traditional data management systems. In many applications, such as in network monitoring, data is most valuable at its generation time and becomes stale quite quickly. Therefore, timely stream processing is often of high economic value, but can also be life saving as in digital health applications. Often

In this talk, after opening the lecture and introducing the logistics, we will discuss efficient stream processing. We will first point out inefficiencies in current stream processing engines and discuss the reason of these inefficiencies in hardware design. We will then explain how to generate more efficient code and, with the example of SIMD computations, discuss portable optimizations for code generation.

About the Speaker

Prof. Dr. Tilmann Rabl works in database research since 2007 and received his Ph.D. from the University of Passau in 2011. After finishing his PhD thesis on the subject of scalability and data allocation in cluster databases, he continued his work as a postdoctoral researcher at the Middleware Systems Research Group at the University of Toronto. In 2015, he joined the Database Systems and Information Management group at Technische Universität Berlin as a senior researcher and visiting professor and held the position of Vice Director of the Intelligent Analytics for Massive Data group at the German Research Center for Artificial Intelligence. Since 2019, he has held the chair for Data Engineering Systems at the Digital Engineering Faculty of the University of Potsdam and the Hasso Plattner Institute. His research focuses on efficiency of database systems, real-time analytics, hardware efficient data processing, and benchmarking.