The research group “Data access and processing” develops innovative methods for storing, managing, and processing large volumes of multi-dimensional data (e.g., from Earth Observation) in distributed IT infrastructures. In particular the storage, management, and analysis of high-resolution time series data from the DLR areas space, aeronautics, transportation, and energy poses huge challenges to database management systems with respect to the expected data volume and velocity. For this, novel database management solutions have to be developed, which can store and process large volumes of time series data (e.g., coming from a large number of sensors). The solution should consider a scalable system infrastructure as well as modern data storage hardware (e.g., SSDs and persistent memory).

The position holder will develop methods and tools for efficiently storing, managing, and analyzing time series and telemetry data from various application domains, such as robotics, connected energy systems, and satellite on-board telemetry data. The focus is on the development of concepts, which can scale to large data volumes and can be adapted to different execution environments (e.g., cloud, edge, embedded). The developed methods shall be integrated into database management systems and accompanied by modern software development processes.

We offer a challenging work and research environment where family friendliness and your healthiness are our highest priorities.

Your tasks include:

• literature survey and identification of research trends in the area of data management and data processing on univariate and multivariate time series data
• identification and preparation of research problems related to the management of large time series data volumes in various execution environments (e.g., cloud, edge)
• development and evaluation of data management systems for time series data based on requirements derived from concrete use cases
• software development for evaluation and integration of modern computer hardware (e.g., novel data storage and processor technologies) in existing time series data management systems or development of time series data management systems based on novel computer hardware technologies
• optimization and specialization of time series data management systems to concrete use cases with the help of performance profiling tools and standardized performance benchmarks
• design and creation of system architectures for complex time series data management systems by using modern software development processes (e.g., Scrum) and incremental adjustments of the system architecture in case of changing user requirements
• identification and development of new application areas with time series data management challenges, which cannot be solved by existing state-of-the-art time series data management systems
• publication and presentation of research results at national and international conferences