Peraic, Martin

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Martin Peraic

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2020 - 20222

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  • Vorschaubild
    Publikation
    On the track to application architectures in public transport service companies
    (MDPI, 2022) Jüngling, Stephan; Fetai, Ilir; Rogger, André; Morandi, David; Peraic, Martin
    There are quite some machine learning (ML) models, frameworks, AI-based services or products from different IT solution providers available, which can be used as building blocks to embed and use in IT solution architectures of companies. However, the path from initial prototypical proof of concept solutions until the deployment of proven systems into the operational environment remains a major challenge. The potential of AI-based software components using ML or knowledge engineering (KE) is huge and the majority of small to medium enterprises are still unsure whether their internal developer teams should be extended by additional ML or KE skills to enrich their IT solution architectures with novel AI-based components where appropriate. How can enterprises manage the change and visualize the current state and foreseeable road-map? In the current paper, we propose an AI system landscape for the public transport sector, which is based on existing AI-domains and AI-categories defined by different technical reports of the European Commission. We collect use-cases from three different enterprises in the transportation sector and visualize them on the proposed domain specific AI-landscape. We provide some insights into different maturity levels of different AI-based components and how the different ML and KE based components can be embedded into an AI-based software development life-cycle (SDLC). We visualize, how the AI-based IT-solution architecture evolved over the last decades with respect to coupling and decoupling of layers and tiers in the overall Enterprise Architecture.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Towards AI-based solutions in the system development lifecycle
    (2020) Jüngling, Stephan; Peraic, Martin; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
    Many teams across different industries and organizations explicitly apply agile methodologies such as Scrum in their system development lifecycle (SDLC). The choice of the technology stack, the programming language, or the decision whether AI solutions could be incorporated into the system design either is given by corporate guidelines or is chosen by the project team based on their individual skill set. The paper describes the business case of implementing an AI-based automatic passenger counting system for public transportation, shows preliminary results of the prototype using anonymous passenger recognition on the edge with the help of Google Coral devices.It shows how different solutions could be integrated with the help of rule base systems and how AI-based solutions could be established in the SDLC as valid and cost-saving alternatives to traditionally programmed software components.
    04B - Beitrag Konferenzschrift