Making industrial analytics work for factory automation applications

In this contribution, we give an insight in our experiences in the technical and organizational realization of industrial analytics. We address challenges in implementing industrial analytics in real-world applications and discuss aspects to consider when designing a machine learning solution for production. We focus on technical and organizational aspects to make industrial analytics work for real-world applications in factory automation. As an example, we consider a machine learning use case in the area of industry compressors. We discuss the importance of scalability and reusability of data analytics pipelines and present a container-based system architecture. Document type: Part of book or chapter of boo

CGIAR Excellence in Breeding Platform - Plan of Work and Budget 2020

At the end of 2019, all CGIAR centers had submitted improvement plans based on an EiB template and in close collaboration with EiB staff while – in a parallel process with breeding programs, funders and private sector representatives – a vision for breeding program modernization was developed and presented to CGIAR breeding leadership at the EiB Annual Meeting. This vision represents an evolution of EiB in the context of the Crops to End Hunger Initiative (CtEH) beyond the initial scope of providing tools, services and expert advice, and serves as a guide for Center leadership to drive changes with EiB support. In addition, EiB has taken the role of managing and disbursing funding, made available by Funders via CtEH to modernize breeding and enable CGIAR breeding programs to implement the vision provided by EiB

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations