Database Systems and Information Management
Lead
Prof. Dr. Volker Markl
Technische Universität Berlin
Einsteinufer 17,
10587
Berlin
Big Data, Data Streaming, Distributed Data
The Distinguished Research Group led by Prof. Dr. Volker Markl addresses the human and technical latencies prevalent in the data analysis process. This entails simplifying the specification of data analysis programs via the automatic distribution, parallelization and hardware adaptation of data processing operations to reduce the human latency and thereby increase programmer productivity. This includes:
- devising intelligent data processing algorithms,
- exploiting novel advances in computer architecture (processing, network, storage),
- building efficient data management, data science, and machine learning technologies as well as systems, to reduce the technical latency and thereby increase execution efficiency and throughput.
Efficient Placement of Decomposable Aggregation Functions for Stream Processing over Large Geo-Distributed Topologies
POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance
Multi-Backend Zonal Statistics Execution with Raven
BIFOLD papers accepted at the CVPR and ESWC 2024
The BIFOLD research project "Unified Processing Model for Distributed Stream Reasoning" released two preprints on Open Distributed Systems, which will be presented at the CVPR 2024 and ESWC 2024 conferences.
“POLAR” lowers the adoption barrier for adaptive query processing in database systems
A preprint by BIFOLD researchers titled "POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance" is set to be presented at the VLDB conference in 2024. The database engineering paper introduces a technique for reordering joins that is adaptive, with a focus on non-invasive integration and low overhead.
EDBT conference 2024
The EDBT conference 2024 took place from March 25 to 28. BIFOLD researchers presented three papers at this Core-A conference on databases: "Benchmarking Stream Join Algorithms on GPUs: A Framework and its Application to the State of the Art", "Bridging the Gap: Complex Event Processing on Stream Processing Engines", and “Evaluation of Sampling Methods for Discovering Facts from Knowledge Graph Embeddings”.