IPE 15-19 Master Thesis or Internship: Scalable storage technologies for large archives of multi-dimensional imaging data
An ever increasing number of large tomographic volumes are recorded at synchrotron facilities worldwide. Due to the drastic increase in data sizes, there is a recent trend to provide data
analysis services at facilities as well. Though a high-speed clustered storage is used to store data sets, it is a challenge to provide the imaging data fast enough to keep applications interactive.
The master thesis will be performed within an international project that aims to build a cloud-based infrastructure for synchrotron light sources enabling remote data analysis and visualization. The objective of this master thesis is to develop the data management system scalable in terms of number of user requests and data volume. The student is expected to evaluate existing technologies developed to handle big data and propose the distributed storage engine, data reduction framework as well as data compression and caching strategies. Additionally, it may be desirable to develop spatialaware
data layout optimized for reading sub-volumes from very large volumes stored at magnetic storage with read performance largely limited by high seek latencies.
The student is expected to be familiar with network administration in Linux and to have a basic know-how on clustering. He should understand the relational and MapReduce models and be aware of current trends in the database technology. Prior experience with TileDB and Apache Spark is a plus.
Institute for Data Processing and Electronics (IPE)
as soon as possible
limited, according to the study regulations
Suren Chilingaryan firstname.lastname@example.org, IPE, Phone: +49 721 / 608 26579
Andreas Kopmann email@example.com, IPE, Phone: +49 721 / 608 24910
Please apply online using the button below for this vacancy number IPE 15-19.
Personnel Support is provided by
Telefon: +49 721 608-25184,
Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
If qualified, severely disabled persons will be preferred.