Automated Machine Learning (Meta AutoML) platforms support data scientists and domain experts by automating the ML model search.A Meta AutoML platform utilizes multiple AutoML solutions searching in parallel for their best ML model.Using multiple AutoML solutions requires a substantial amount of energy.While AutoML solutions utilize different training strategies to optimize their energy efficiency and ML model effectiveness, no research has yet addressed optimizing the Meta AutoML process.This paper presents a survey of 14 AutoML training strategies that can be applied to Meta AutoML.The survey categorizes these strategies by their broader goal, their advantage and Meta AutoML adaptability.This paper also introduces the concept of rule-based training strategies and a proof-of-concept implementation in the Meta AutoML platform OMA-ML.This concept is based on the blackboard architecture and uses a rule-based reasoner system to apply training strategies.Applying the training strategy "top-3" can save up to 70% of energy, while maintaining a similar ML model performance.

The integrated cyber-physical systems in Smart Manufacturing generate continuously vast amount of data. These complex data are difficult to assess and gather knowledge about the data. Tasks like fault detection and diagnosis are therewith difficult to solve. Visual Analytics mitigates complexity through the combined use of algorithms and visualization methods that allow to perceive information in a more accurate way. Thereby, reasoning relies more and more on the given situation within a smart manufacturing environment, namely the context. Current general Visual Analytics approaches only provide a vague definition of context. We introduce in this paper a model that specifies the context in Visual Analytics for Smart Manufacturing. Additionally, our model bridges the latest advances in research on Smart Manufacturing and Visual Analytics. We combine and summarize methodologies, algorithms and specifications of both vital research fields with our previous findings and fuse them together. As a result, we propose our novel industry 4.0-ready Visual Analytics model for context-aware diagnosis in Smart Manufacturing.

Software cities alias application landscapes of large enterprises comprise tens or even hundreds of IT applications. Structuring software cities into domains is an important task of enterprise architects. The quality of the resulting domain model is crucial for the success of enterprise architecture management and an important tool for the governance of the development of an enterprise's application landscape. This paper presents a novel method for constructing domain models based on business services, business objects, and business dimensions. The method has been validated in numerous industrial projects.