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TU Berlin and DFKI Papers Presented at EDBT 2020

Researchers in DFKI’s Intelligent Analytics for Massive Data Group and TU Berlin’s Database Systems and Information Management (DIMA) Group presented three systems papers at EDBT 2020, the 23rd International Conference on Extending Database Technology, held from March 30 to April 2. Originally planned to take place in Copenhagen, Denmark, this year’s EDBT conference was held online instead.

DIMA PhD Student, Haralampos Gavriilidis presented “Scaling a Public Transport Monitoring System to Internet of Things Infrastructures”. In the talk, Harry casts a public transport problem under an IoT scenario, discusses some IoT data management challenges, motivates the need for the development of a novel platform for the end-to-end data management for the IoT (NebulaStream), and demonstrates how an interactive map can be used to monitor a public transport system. The paperand video of the demo is available at

DIMA PhD Student, Ankit Chaudhary presented “Governor: Operator Placement for a Unified Fog-Cloud Environment.” In his talk, Ankit motivates the need for a unified fog-cloud environment in the IoT, presents the operator placement problem in light of service-level agreements, introduces Governor, a novel operator placement approach for a unified fog-cloud environment, and discusses Governor Policies (GP) to optimize operator placement in user queries and enable administrators to control the operator placement process. In addition, he offers a demonstration to highlight the impact GP have on operator placement for varying queries. The paper and video of the demo is available at

Former DIMA Master’s student, Lawrence Benson presented “Disco: Efficient Distributed Window Aggregation”, a short paper based on his Master’s thesis. Disco is a distributed complex window aggregation approach designed to process complex window types on multiple independent nodes, while efficiently aggregating incoming data streams. In his talk, Lawrence highlights the advantages Disco offers over centralized solutions, including the throughput scales linearly with the number of nodes as well as significantly reducing network costs. The paper and video of the talk is available at

All of the research conducted in these works were conducted under the auspices of the Berlin Institute for the Foundations of Learning and Data (BIFOLD).