I work on query processing in databases, and the use of artificial intelligence to boost database performance. Regarding the application of databases, I investigate the potential of collecting the queries first and then derive the database design from them.
Research projects
Query-driven database design, integration, and optimization
Using modern hardware (FPGAs) to speed up database-query processing
Investigate AI methods for the improvement of database technology (e.g. autoencoder)
Build a repository of neural-ne
SKYSHARK -Benchmarking Data Processing Systems Using Real-Time Flight Data
To test and evaluate a heterogeneous stream-processing system consisting of an FPGA-based systemon-chip and a host, we develop a benchmark called SKYSHARK. It uses real-world data from air-traffic control that is publicly available. These data are enhanced for the purpose of the benchmark without changing their characteristics. They are further enriched with aircraft and airport data. We define 14 queries with respect to the particular requirements of our system. They should be useful for other hardware-accelerated platforms as well. A first evaluation has been done using Apache Flink. We envision a great potential because of the flexibility of the approach.
With the ongoing rise in global data volumes, database compression is becoming increasingly relevant. While the compression of numeric data types has been extensively researched, the compression of strings has only recently received renewed scientific attention.
A promising approach to string compression is the use of symbol tables, where recurring substrings within a database are substituted with short codes. A corresponding table enables the smooth reconstruction of the original data. This method is distinguished by short compression and decompression times, although the compression rate heavily depends on the quality of the symbol table.
The research project FST focuses on the creation of optimized symbol tables to maximize the compression rate. For this purpose the eponymous Frequent-Substring Trees are constructed, a trie-like data structure that maps all potential table entries and enables the identification of optimal entries through the use of metadata.
The primary objective of the research project is to increase the compression rate of string compression methods without significantly affecting the compression and decompression times.
Benenson, Z., Freiling, F., & Meyer-Wegener, K. (2022). Soziotechnische Einflussfaktoren auf die "digitale Souveränität" des Individuums. In Glasze, Georg; Odzuck; Eva; Staples, Ronald (Hrg.), Was heißt digitale Souveränität? Diskurse, Praktiken und Voraussetzungen "individueller" und "staatlicher Souveränität" im digitalen Zeitalter. (S. 61 - 87). Bielefeld: transcript Verlag.
Beena Gopalakrishnan Nair, L., Becher, A., & Meyer-Wegener, K. (2020). The ReProVide Query-Sequence Optimization in a Hardware-Accelerated DBMS. In DaMoN '20: Proceedings of the 16th International Workshop on Data Management on New Hardware (pp. 1-3). Portland, Oregon USA: ACM Digital Library.
I work on query processing in databases, and the use of artificial intelligence to boost database performance. Regarding the application of databases, I investigate the potential of collecting the queries first and then derive the database design from them.
Research projects
SKYSHARK -Benchmarking Data Processing Systems Using Real-Time Flight Data
(Own Funds)
URL: https://skyshark.org/
To test and evaluate a heterogeneous stream-processing system consisting of an FPGA-based systemon-chip and a host, we develop a benchmark called SKYSHARK. It uses real-world data from air-traffic control that is publicly available. These data are enhanced for the purpose of the benchmark without changing their characteristics. They are further enriched with aircraft and airport data. We define 14 queries with respect to the particular requirements of our system. They should be useful for other hardware-accelerated platforms as well. A first evaluation has been done using Apache Flink. We envision a great potential because of the flexibility of the approach.
Generation of Symbol Tables for String Compression with Frequent-Substring Trees
(Own Funds)
With the ongoing rise in global data volumes, database compression is becoming increasingly relevant. While the compression of numeric data types has been extensively researched, the compression of strings has only recently received renewed scientific attention.
A promising approach to string compression is the use of symbol tables, where recurring substrings within a database are substituted with short codes. A corresponding table enables the smooth reconstruction of the original data. This method is distinguished by short compression and decompression times, although the compression rate heavily depends on the quality of the symbol table.
The research project FST focuses on the creation of optimized symbol tables to maximize the compression rate. For this purpose the eponymous Frequent-Substring Trees are constructed, a trie-like data structure that maps all potential table entries and enables the identification of optimal entries through the use of metadata.
The primary objective of the research project is to increase the compression rate of string compression methods without significantly affecting the compression and decompression times.
2025
2023
2022
2021
2020
Related Research Fields
Contact: