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Auflistung nach Autor:in "Thomas,Oliver"

1 - 5 von 5
  • Textdokument
    Analyzing Smart Services from a (Data-) Ecosystem Perspective: Utilizing Network Theory for a graph-based Software Tool in the Domain Smart Living
    (INFORMATIK 2022, 2022) Kortum,Henrik; Hagen,Simon; Hühn,Janis; Thomas,Oliver
    There has long been a trend away from monolithic solutions toward integrated service systems that combine technical services and product functionalities across different manufacturers. This is especially true for the Smart Living domain, where interconnected products and services are used in one of the most private areas. However, there is a lack of tools to manage these complex systems. This work represents the second iteration of an ongoing research project in which we are developing an analysis and management tool in response to specific requirements of various stakeholder groups of the Smart Living domain. We interpret the services and products of the data ecosystem as nodes of a network graph, which we analyze using established methods of network theory, e.g., to identify weak points and bottlenecks in the service systems. The metrics provided help domain experts and managers from the domain to perform concrete tasks and provide a business benefit.
  • Textdokument
    Learning without Looking: Similarity Preserving Hashing and Its Potential for Machine Learning in Privacy Critical Domains
    (INFORMATIK 2022, 2022) Eleks,Marian; Rebstadt,Jonas; Fukas,Philipp; Thomas,Oliver
    Machine Learning is frequently ranked as one of the most promising technologies in several application domains but falls short when the data necessary for training is privacy-sensitive and can thus not be used. We address this problem by extending the field of Privacy Aware Machine Learning with the application of Similarity Preserving Hashing algorithms to the task of data anonymization in a Design Science Research approach. In this endeavor, novel anonymization algorithms made to enable Machine Learning on anonymized data are designed, implemented, and evaluated. Throughout the Design Science Research process, we present a collection of issues and requirements for Privacy Aware Machine Learning algorithms along with three Similarity Preserving Hashing-based algorithms to fulfil them. A metric-based comparison of established and novel algorithms as well as new arising opportunities for Machine Learning on sensitive data are also added to the current knowledge base of Information Systems research.
  • Textdokument
    A Platform Framework for the Adoption and Operation of ML-based Smart Services in the Data Ecosystem of Smart Living
    (INFORMATIK 2022, 2022) Kortum,Henrik; Kohl,Tobias; Hubertus,Dominik; Hinz,Oliver; Thomas,Oliver
    Smart services utilizing machine learning (ML) take a more and more important position in our daily lives. As a result, the need for a large smart living data ecosystem has emerged that links the most diverse areas of life with each other. This ecosystem is characterized by a multitude of different actors, a heterogeneous system, product and service landscape as well as high data protection requirements. To provide real added value and holistic solutions in this tension field, the orchestration of different subservices is necessary, bundling the functionality of individual smart devices and models. For this goal to be achieved, a framework that considers the complex challenges of the ecosystem focusing on the adoption and operation of smart services is required. Here our paper makes a key contribution. Based on requirements from the literature and concrete smart living use cases, we derive a platform framework for this data ecosystem.
  • Textdokument
    Service Tailoring: Ein Konfigurationsmechanismus für individualisierte After-Sales-Services im Maschinen- und Anlagenbau
    (INFORMATIK 2022, 2022) Brinker,Jonas; Kammler,Friedemann; Thomas,Oliver
    Außerplanmäßige Instandsetzungen sind eine Herausforderung im Maschinen- und Anlagenbau. Dies betrifft sowohl Nutzer der Maschinen, die aufgrund der Ausfallzeit Abweichungen von ihren Produktionsplänen hinnehmen, aber auch Hersteller, die Serviceressourcen kurzfristig und dynamisch zuordnen müssen. Der Einsatz technischer Innovationen eröffnet dabei neue Serviceoptionen, die beispielsweise vorteilhaft hinsichtlich Zeitaufwands, Kosten oder Nachhaltigkeit sind. Gleichzeitig variieren die kundenindividuellen Anforderungen an die Serviceerbringung, sowohl hinsichtlich der benötigten Ressourcen als auch hinsichtlich individueller Präferenzen. Mit dem „Service Tailoring“ (ST) stellen wir einen Mechanismus vor, wie Serviceoptionen kundenspezifisch konfiguriert werden können. Aus elf Experteninterviews erheben wir zunächst Prozessvarianten in der Instandhaltung und bilden einen generalisierten Lösungsraum. Indem wir Mechanismen der Mass-Customization (MC) als Constraint-Optimization-Problem adaptieren, entwickeln wir einen zweistufigen methodischen Vorschlag und demonstrieren diesen an einem Beispiel aus der Landtechnik.
  • Textdokument
    Towards the Operationalization of Trustworthy AI: Integrating the EU Assessment List into a Procedure Model for the Development and Operation of AI-Systems
    (INFORMATIK 2022, 2022) Kortum,Henrik; Rebstadt,Jonas; Böschen,Tula; Meier,Pascal; Thomas,Oliver
    Artificial intelligence (AI) is increasingly permeating all areas of life and not only changing coexistence in society for the better. Unfortunately, there is an increasing number of examples where AI systems show problematic behavior, such as discrimination or insufficient accuracy, missing data privacy or transparency. To counteract this trend, an EU initiative has drafted a legal framework and recommendations on how AI can be more trustworthy and comply with people's fundamental rights. However, fundamental rights are currently not reflected in procedure models for the development and operation of AI systems. Our work contributes to closing this gap so that companies, especially SMEs with small IT departments and limited financial resources, are supported in the development process. Within the framework of a structured literature review, we derive a procedure model for the development and operation of AI systems and subsequently integrate concrete recommendations for achieving trustworthiness.