# On the Track to Application Architectures in Public Transport Service Companies
## 1 Introduction  
When looking at the evolution of software architectures that started from mainframes and dumb 3270 terminals to N-Tier architectures, the distribution of specialized functionality to dedicated hardware-related tiers such as database servers or logical layers such as the presentation layer, which was triggered by a substantial technology gap between HTML as a markup language for the graphical user interface (GUI) and the object-oriented programming languages used to implement the application- and business-logic. With an increasing focus of organizations to business processes, workflow and rule engines were incorporated into the software architectures, leading to additional layers or tiers. With the increasing capabilities of artificial intelligence (AI), which in itself can be decomposed into machine learning (ML) and Knowledge Engineering (KE) sub-domains, additional layers and tiers slowly establish and need to be incorporated into the overall IT architecture and the software development life-cycle (SDLC) of all these different building blocks. In case of ML models, an additional challenge is added to the life-cycle management of components, which is equally severe as the gap between the markup and the programminglanguages in the past. The models no longer can be designed, developed, tested and put into production when being compliant to their specification, but their quality is rather validated based on probability scores and, even more challenging, can modify its behavior or learn during its operation and potentially degrade over time due to data and concept shifts [1]. Such scenarios are new and require not only an additional focus to the initial maturity of the components but are also challenging the maturity of an organization with respect to managing these components over it's life-cycle. How can an enterprise manage these issues? What are appropriate changes in the software development life-cycle when AI-based components, that are trained instead of programmed need to be incorporated in the IT-Architecture? How can the maturity of the components be tracked during an experiment-driven and data-driven development process? How can it be monitored in the runtime environment? We will analyse and visualize these changes from a structural as well as a behavioural perspective. Maturity models have been applied in many different domains. It is quite common to specify the maturity of organizations with respect to business process management, whether the processes are explicitly modeled with BPMN [2], implemented with the help of workflow engines. It is even possible to extract dedicated business rules from the processes. The rules can be modelled with the decision model and notation DMN [3] and handed over back to the responsibility of the business units, where they can be quickly modified according to their needs. Maturity models such as the CMM or BPMM have been developed in order to characterize the different awareness level, to which software development processes or business processes are managed in enterprises [4]. More recently, maturity models seem to gain focus for the assessment of AI-based software components [5] such as the one of Gartner [6] or Forrester [7]. Based on general AI-categories and domain specific railway related sub-themes, we first focus on creating an overview of AI-based components in public transport service industries from a structural perspective, resulting in a domain-specific AI-landscape. Second, we visualize, how AI-based functionalities are embedded in appropriate layers and tiers in the context of a domain-independent evolution of the overall IT Architecture. Third, we provide domain-specific examples and different use-cases of AI-based components, how they are developed and embedded into the organization's software development life-cycle from a behavioral perspective, which is driven by the different maturity levels from initial prototypical implementations until the operation of AI-based components in production.