Welcome to the Database Research Group
The Database Research Group is part of the Department of Computer Science
at the University of Salzburg, Austria.
In our research, we deal with all aspects of data management. We are attracted by applications that are heavily based on data but cannot leverage current systems due to the rich set of queries they need. The focus of our research is on queries over complex objects and massive data collections, data cleaning and integration, indexing techniques, query processing and optimization, distributed data management, and numerical computations in databases. Our research is triggered by problems that arise in concrete applications, for example, process mining, digital humanities, or cognitive neuroscience. The results of our research are new algorithms with performance guarantees, which are implemented and evaluated on the motivating application.
Head of the Database Research Group
Deputy Head of the Database Research Group
Second Paper at VLDB 2023
Our paper "A Two-Level Signature Scheme for Stable Set Similarity Joins" has been accepted at the International Conference on Very Large Data Bases (VLDB) 2023.
Best Artifact Award at SIGMOD 2023
The reproducibility package of our paper "JEDI: These aren't the JSON documents you're looking for..." won the "Best Artifact Award" at the ACM International Conference on Management of Data (SIGMOD) 2023.
Paper at DataEd 2023
Our paper "Feedforward-Aided Course Designs for Similarity Search" has been accepted at the International Workshop on Data Systems Education (DataEd) 2023.
Paper at VLDB 2023
Our paper "Benchmarking the Utility of w-event Differential Privacy Mechanisms – When Baselines Become Mighty Competitors" has been accepted at the International Conference on Very Large Data Bases (VLDB) 2023.
Paper at SIGMOD 2023
Our paper "FINEX: A Fast Index for Exact & Flexible Density-Based Clustering" has been accepted at the ACM International Conference on Management of Data (SIGMOD) 2023.
Three Papers at ICDE 2023
Our papers "MetricJoin: Leveraging Metric Properties for Robust Exact Set Similarity Joins", "Benchmarking Filtering Techniques for Entity Resolution", and "KOIOS: Top-k Semantic Overlap Set Search" have been accepted at the IEEE International Conference on Data Engineering (ICDE) 2023.
Thomas Hütter talks about our JSON Edit Distance (JEDI) on Jack Waudby's Disseminate Podcast