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Unsere unternehmerische Umwelt befindet sich in einem zunehmend dynamischen Wandel. Dies führt dazu, dass Herausforderungen, denen sich Unternehmen stellen müssen, immer komplexer werden. Hier gilt es zunehmend, eine Balance zwischen verschiedenen Spannungsfeldern zu erreichen. Sogenannte Megatrends stellen die Treiber dieses Wandels dar. Als Megatrend werden nach dem Zukunftsinstitut (2010a) richtungsweisende Veränderungstendenzen aufgefasst, die alle Bereiche des Lebens sowohl individuell als auch gesellschaftlich beeinflussen und langfristige Auswirkungen haben.
A concept for the analysis and optimal design of reinforced concrete structures is described. It is based on a nonlinear optimization algorithm and a finite element program for linear and nonlinear analysis of structures. With the aim of minimal cost design a two stage optimization using efficient gradient algorithm is developed. The optimization problems on global (structural) and local (crosssectional) level are formulated. A parallelization concept for solving the two stage optimization problem in minimal time is presented. Examples are included to illustrate the practical use and the effectively of the parallelization in the area of engineering design.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.