11 Interfakultäre Einrichtungen
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/12
Browse
2 results
Search Results
Item Open Access Concepts and methods for the design, configuration and selection of machine learning solutions in manufacturing(2021) Villanueva Zacarias, Alejandro Gabriel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The application of Machine Learning (ML) techniques and methods is common practice in manufacturing companies. They assign teams to the development of ML solutions to support individual use cases. This dissertation refers as ML solution to the set of software components and learning algorithms to deliver a predictive capability based on available use case data, their (hyper) paremeters and technical settings. Currently, development teams face four challenges that complicate the development of ML solutions. First, they lack a formal approach to specify ML solutions that can trace the impact of individual solution components on domain-specific requirements. Second, they lack an approach to document the configurations chosen to build an ML solution, therefore ensuring the reproducibility of the performance obtained. Third, they lack an approach to recommend and select ML solutions that is intuitive for non ML experts. Fourth, they lack a comprehensive sequence of steps that ensures both best practices and the consideration of technical and domain-specific aspects during the development process. Overall, the inability to address these challenges leads to longer development times and higher development costs, as well as less suitable ML solutions that are more difficult to understand and to reuse. This dissertation presents concepts to address these challenges. They are Axiomatic Design for Machine Learning (AD4ML), the ML solution profiling framework and AssistML. AD4ML is a concept for the structured and agile specification of ML solutions. AD4ML establishes clear relationships between domain-specific requirements and concrete software components. AD4ML specifications can thus be validated regarding domain expert requirements before implementation. The ML solution profiling framework employs metadata to document important characteristics of data, technical configurations, and parameter values of software components as well as multiple performance metrics. These metadata constitute the foundations for the reproducibility of ML solutions. AssistML recommends ML solutions for new use cases. AssistML searches among documented ML solutions those that better fulfill the performance preferences of the new use case. The selected solutions are then presented to decision-makers in an intuitive way. Each of these concepts was evaluated and implemented. Combined, these concepts offer development teams a technology-agnostic approach to build ML solutions. The use of these concepts brings multiple benefits, i. e., shorter development times, more efficient development projects, and betterinformed decisions about the development and selection of ML solutions.Item Open Access Metadata management in virtual product development to enable cross-organizational data analytics(2024) Ziegler, Julian; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)Due to the advancing digitalization, companies are increasingly adopting computer-aided technologies. Especially in product development, computer-aided technologies enable a gradual shift from physical to virtual prototypes. This shift towards virtual product development includes design, simulation, testing, and optimization of products, and reduces costs and time needed for these tasks. Companies with strong activities in the field of virtual product development generate large amounts of heterogeneous data and wish to mine these data for knowledge. In this context, metadata is a key enabler for data discovery, data exploration, and data analyses but often neglected. The diversity in the structure and formats of virtual product development data makes it difficult for domain experts to analyze them. Domain experts struggle with this task because such engineering data are not sufficiently described with metadata. Moreover, data in companies are often isolated in data silos and difficult to explore by domain experts. This calls for an adequate data and metadata management that is able to cope with the significant data heterogeneity in virtual product development, and that empowers domain experts to discover and access data for further analyses. This thesis identifies previously unsolved challenges for a data and metadata management that is tailored to virtual product development and makes three contributions. First, a metadata model that provides a connected view on all data, metadata, and work activities of virtual product development projects. A prototypical implementation of this metadata model is already being applied to a real-world use case of an industry partner. Based on this foundation, the second contribution uses this metadata model to enable feature engineering with domain experts as part of data analyses projects. Going further, data analyses can directly use the metadata structure to provide added value without having to access the large amounts of product data. To this end, the third contribution utilizes the metadata structure itself to enable a novel approach to process discovery for product development projects. Thus, process structures in development projects can be analyzed with little effort, e.g., to identify good or inefficient processes in development projects.