The “Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines,” paper 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 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 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 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.