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Database Systems and Information Management GroupFour papers authored by TU Berlin and DFKI researchers have been accepted at SIGMOD 2020

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Four papers authored by TU Berlin and DFKI researchers have been accepted at SIGMOD 2020

Data management systems researchers in the Database Systems and Information Management (DIMA) Group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) Group at DFKI (the German Research Institute for Artificial Intelligence) were informed that their papers have been accepted at the 2020 ACM SIGMOD/PODS International Conference on the Management of Data.

The “Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines,” paper [1] authored by Del Monte et al. addresses the problem of large state migration and on-the-fly query reconfiguration, to support resource elasticity, fault-tolerance, and runtime optimization (e.g., for load balancing). A stream processing engine equipped with Rhino is capable of attaining lower latency processing and achieving continuous operation, even in the presence of failures.

The “Optimizing Machine Learning Workloads in Collaborative Environments, paper [2] authored by Derakhshan et al. presents a system that is capable of optimizing the execution of machine learning workloads in collaborative environments. This accomplishment is achieved by exploiting an experiment graph of stored artifacts drawn from previously performed operations and results.

The “Grizzly: Efficient Stream Processing Through Adaptive Query Compilation,” paper [3] authored by Grulich et al. presents a novel adaptive query compilation-based stream processing engine that enables highly-efficient query execution on modern hardware and is able to dynamically adjust to changing data characteristics at runtime.

The Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects,” paper [4] authored by Lutz et al. provides an in-depth analysis of the new NVLink 2.0 interconnect technology, which enables users to overcome data transfer bottlenecks and efficiently process large datasets stored in main-memory on GPUs.

The parallel acceptance of these four publications at one of the top data management conferences is not only a great success for TU Berlin’s DIMA Group and DFKI’S IAM Group, it also shows that BIFOLD, the Berlin Institute for the Foundations of Learning and Data continues to positively impact international artificial intelligence and data management research.

For more information about BIFOLD visit: http://bifold.berlin/ and for more details about the annual SIGMOD conference visit: https://sigmod2020.org/.

 

References

[1] “Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines,” Bonaventura Del Monte, Steffen Zeuch, Tilmann Rabl, and Volker Markl, https://bit.ly/3dbgKPz.

[2] “Optimizing Machine Learning Workloads in Collaborative Environments,“ Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Ziawasch Abedjan, Tilmann Rabl, and Volker Markl, https://bit.ly/3ddYxkE.

[3] “Grizzly: Efficient Stream Processing Through Adaptive Query Compilation,” Philipp Grulich, Sebastian Breß, Steffen Zeuch, Jonas Traub, Janis von Bleichert, Zongxiong Chen, Tilmann Rabl, and Volker Markl, https://bit.ly/2U3sAnv.

[4] “Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects,“ Clemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, and Volker Markl, https://bit.ly/2QuseUx.

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