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Auflistung nach Autor:in "Pelzel, Frank"

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  • Textdokument
    A Reference Architecture for On-Premises Chatbots in Banks and Public Institutions
    (INFORMATIK 2021, 2021) Koch, Christian; Linnik, Benjamin; Pelzel, Frank; Sultanow,Eldar; Welter, Sebastian; Cox, Sean
    Chatbots have the potential to significantly increase the efficiency of banks and public institutions. Both sectors, however, are subject to special regulations and restrictions in areas such as information security and data protection. The policies of these organizations therefore, in some cases, reject the use of cloud and proprietary products because in their view they lack transparency. As a result, the implementation of chatbots in banks and public institutions often focuses on open-source and on-premises solutions; however, there are hardly any scientific guidelines on how to implement these systems. Our paper aims to close this research gap. The article proposes a reference architecture for chatbots in banks and public institutions that are a.) based on open-source software and b.) are hosted on-premises. The framework is validated by case studies at TeamBank AG and the German Federal Employment Agency. Even if our architecture is designed for these specific industries, it may also add value in other sectors – as chatbots are expected to become increasingly important for the practical application of artificial intelligence in enterprises.
  • Textdokument
    Software Architecture Best Practices for Enterprise Artificial Intelligence
    (INFORMATIK 2020, 2021) Martel, Yannick; Roßmann, Arne; Sultanow, Eldar; Weiß, Oliver; Wissel, Matthias; Pelzel, Frank; Seßler, Matthias
    AI systems are increasingly evolving from laboratory experiments in data analysis to increments of productive software products. A professional AI platform must therefore not only function as a laboratory environment but must be designed and procured as a workbench for the development, productive implementation, operation and maintenance of ML models. Subsequently, it needs to integrate within a global software engineering approach. This way, Enterprise Architecture Management (EAM) must implement efficient governance of the development cycle, to enable organization-wide collaboration, to accelerate the go-live and to standardize operations. In this paper we highlight obstacles and show best practices on how architects can integrate data science and AI in their environment. Additionally, we suggest an integrated approach adapting the best practices from both the data science and DevOps.