A Machine Learning Based Approach to Application Landscape Documentation
Abstract
In the era of digitalization, IT landscapes keep growing along with complexity and dependencies. This amplifies the need to determine the current elements of an IT landscape for the management and planning of IT landscapes as well as for failure analysis. The field of enterprise architecture documentation sought for more than a decade for solutions to minimize the manual effort to build enterprise architecture models or automation. We summarize the approaches presented in the last decade in a literature survey. Moreover, we present a novel, machine-learning based approach to detect and to identify applications in an IT landscape.
| Attribute | Value |
|---|---|
| Address | Wien |
| Authors | Dr. Jörg Landthaler , Dr. Ömer Uludağ , Dr. Gloria Bondel , Dr. Ahmed Elnaggar , Saasha Nair , Prof. Dr. Florian Matthes |
| Citation | Landthaler, J.; Uludağ, Ö.; Bondel, G.; Elnagger, A.; Nair, S.; Matthes, F.: A Machine Learning Based Approach to Application Landscape Documentation, 11th IFIP WG 8.1 working conference on the Practice of Enterprise Modelling (PoEM), Vienna, Austria, 2018 |
| Key | La18b |
| Research project | |
| Title | A Machine Learning Based Approach to Application Landscape Documentation |
| Type of publication | Conference |
| Year | 2018 |
| Project | Multi-Level Monitoring Visualization |
| Acronym | ML4EAM |
| Publication URL | |
| Team members |