On the Track to Application Architectures in Public Transport Service Companies
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 s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/12/6073 |
| _version_ | 1797490246307282944 |
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| author | Stephan Jüngling Ilir Fetai André Rogger David Morandi Martin Peraic |
| author_facet | Stephan Jüngling Ilir Fetai André Rogger David Morandi Martin Peraic |
| author_sort | Stephan Jüngling |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:29:15Z |
| format | Article |
| id | doaj.art-197b974d97c14a9fa0657ba630d1be20 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:29:15Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-197b974d97c14a9fa0657ba630d1be202023-11-23T15:27:22ZengMDPI AGApplied Sciences2076-34172022-06-011212607310.3390/app12126073On the Track to Application Architectures in Public Transport Service CompaniesStephan Jüngling0Ilir Fetai1André Rogger2David Morandi3Martin Peraic4School of Business, University of Applied Sciences and Northwestern Switzerland (FHNW), 4052 Basel, SwitzerlandSwiss Federal Railways (SBB), Competence Center on Machine Perception, 3014 Bern, SwitzerlandSwiss Federal Railways (SBB), Competence Center on Machine Perception, 3014 Bern, SwitzerlandRegional Transport Bern-Solothurn (RBS), 3048 Worblaufen, SwitzerlandSchool of Business, University of Applied Sciences and Northwestern Switzerland (FHNW), 4052 Basel, SwitzerlandThere 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.https://www.mdpi.com/2076-3417/12/12/6073AI architectureAI maturity modelAI landscapemachine learningknowledge engineeringsoftware development life-cycle |
| spellingShingle | Stephan Jüngling Ilir Fetai André Rogger David Morandi Martin Peraic On the Track to Application Architectures in Public Transport Service Companies Applied Sciences AI architecture AI maturity model AI landscape machine learning knowledge engineering software development life-cycle |
| title | On the Track to Application Architectures in Public Transport Service Companies |
| title_full | On the Track to Application Architectures in Public Transport Service Companies |
| title_fullStr | On the Track to Application Architectures in Public Transport Service Companies |
| title_full_unstemmed | On the Track to Application Architectures in Public Transport Service Companies |
| title_short | On the Track to Application Architectures in Public Transport Service Companies |
| title_sort | on the track to application architectures in public transport service companies |
| topic | AI architecture AI maturity model AI landscape machine learning knowledge engineering software development life-cycle |
| url | https://www.mdpi.com/2076-3417/12/12/6073 |
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