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Auflistung nach Autor:in "Kuehnel, Stephan"

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  • Textdokument
    “HySLAC” – A Conceptual Model for Service Level Agreement Compliance in Hybrid Cloud Architectures
    (INFORMATIK 2020, 2021) Seifert, Michael; Kuehnel, Stephan
    Cloud computing provides IT infrastructures and services via networks, and it enables economic potentials for end users as well as a focus on core competencies. In addition to its extensive potentials, cloud computing in general, and hybrid cloud computing in particular, pose new challenges in the negotiation and formulation of Service Level Agreements, as well as in the monitoring of and compliance with contractual requirements. An understanding of cloud service and deployment models, perspectives, roles, and contractual terms is essential for a successful and compliant adoption of hybrid clouds. Consequently, this paper proposes the novel HySLAC model, focusing on service level agreement compliance in hybrid cloud architectures. Based on eight model requirements and a systematic literature review, the HySLAC model was conceptualized with UML 2.0. It comprises eight UML classes and five associated enumerations, and it is instantiated by means of a case study. The model offers scientific and practical application capabilities for the analysis of service components as well as hybrid cloud service compositions, and it opens up potentials for decision support.
  • Konferenzbeitrag
    Towards Identifying GDPR-Critical Tasks in Textual Business Process Descriptions
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Nake, Leonard; Kuehnel, Stephan; Bauer, Laura; Sackmann, Stefan
    Complying with data protection regulations is an essential duty for organizations since violating them would lead to monetary penalties from authorities. In Europe, the General Data Protection Regulation (GDPR) defines personal data and requirements for dealing with this type of data. Hence, organizations must identify business activities that deal with personal data to establish measures to fulfill these requirements. Especially for large organizations, a manual identification can be labor-intensive and error-prone. However, textual business process descriptions, such as work instructions, provide valuable insights into the data used in organizations. Therefore, we propose a first approach to automatically identify GDPR-critical tasks in textual business process descriptions. More specifically, we use a supervised machine learning algorithm to automatically identify whether a task deals with personal data or not. A first evaluation of our approach with a dataset of 37 process descriptions containing 509 activities demonstrates that our approach generates satisfactory results.