Architectures for heavy duty data processing

Geoprocessing refers to the data processing that is performed to transform, merge, analyze and visualize data from different sources. The increasing volume and variety of data, as well as the velocity of data streams, require new and advanced methods, technologies and architectural designs to cope with these challenges. This is where our Efficient Processing Lab contributes with research and development (R&D) and professional services (PS).
Matthes Rieke and his team work on:

• Processing Scalability: identifying and realizing requirements for horizontal and vertical scaling of processing algorithms.
• Workflows Chains and Orchestration: designing and automating complex process workflows.
• Earth Observation Data Processing: handling huge amounts of EO data in an efficient and scalable way.
• Cloud Environments (PaaS, IaaS, SaaS): enabling their possibilities with regards to deployment patterns, handling of input data and processing results.
• Standardization: harmonizing processing interfaces, creating and using interoperable data formats.
• Processing Transparency: strengthening reproducibility, data quality and metadata quality, as well as process discovery.

We address these challenges in a number of R&D and PS projects. Our research partners and customers are from academia and industry covering various application domains, for example, environmental monitoring, agricultural applications, or disaster management. This diversity enables us to develop new approaches that take into account the requirements of many real-world problems and use cases.ctures for heavy duty data processing