This is an overview of the most prominent work the Database Group Mittweida and our project collaborators are working on. While most results are prototypes, we still use them for research and industry projects.

Pandemic Simulation

The goal of this simulation is to show and to study effects of different actions in an pandemic crisis, when a virus jumps over from human to human, such as COVID-19. Actions include social distancing.

More details are available on this work page.

Distributed Analytics Infrastructure – The Personal Health Train Approach

In recent years, several approaches have been developed allowing to analyze large data collection that are distributed over several locations and, thus, systems under privacy constraints. We are part of an international team working on the Personal Health Train approach. Together with partners at the RWTH Aachen and University of Tübingen, we are implementing the German train infrastructure. While this infrastructure focuses on medical applications – as the names implies – it is generic and can be used in other domains too. We are looking for such applications in other domains, such as forensics.

More details are available on this work page.

Privacy Preserving Record Linkage (PPRL)

The goal of PPRL techniques is to link horizontally partitioned data referring to the same person. Such techniques can be used, whenever person data relies in different data sources and need to be merged to analyze them. Such scenarios exist in difference domains including medical sciences but also crime detection.

More details are available on this work page.

Storage and Analysis of Spatial-Temporal Data (stempo)

The goal of the stempo project is the conception and implementation of a system for the data protection compliant storage of spatial-temporal data and their connected sensor data (e.g. heart rate). Besides the development of this storage solution, algorithms for analyzing spatio-temporal data are developed as well.

More details are available on this work page.

Clustering Based Place Recognition (CluPlaR)

The CluPlaR algorithm combines individual geo-positions (PositionVisit) for place visits (PlaceVisit) and their connections (Track). Fundamental for this aggregation is the clustering of the positions based on the time spent at a location and its radius. The PlaceVisit graph is created by the aggregation, which shows the chronological sequence of the place visits. Based on this graph, the place visits can be further aggregated to locations (Place) that are connected by Connections. The resulting PlaceGraph is time-independent.

More details are available on this work page.

Key-Logger

A challenge in large organization is often to get access to rooms which are normally locked. While there are sophisticated access solutions available in which users using a specific card or device (sometimes called transponder) to unlock and lock the door, we have built a key lending system logging which user have lent which key for which room.

More details are available on this work page.