Master Thesis or Internship: Data Partitioning for Large Scientific Databases
This job is a master thesis or internship opportunity at the Institute for Process Data Processing and Electronics (IPE). The focus of the project is to develop an efficient and robust database partitioning strategy for managing vast amounts of data generated by large-scale scientific experiments. The project offers a remarkable opportunity for skill development and knowledge enhancement in the domain of advanced database management.
- Understand the complexities of large-scale scientific data and the challenges it poses to traditional database systems
- Evaluate the performance difference between vertical partitioning and horizontal partitioning
- Investigate and evaluate existing database partitioning techniques in the context of scientific data
- Design and implement novel database partitioning algorithms tailored to the specific requirements of scientific experiments
- Optimize data access and retrieval for faster query performance and enhanced data management
- Familiarity with programming languages such as Python or C++ or C#
- Basic knowledge of database systems
- Practical experience with SQL databases is advantageous but not mandatory
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