A machine learning based approach to application landscape documentation

Jörg Landthaler, Ömer Uludağ, Gloria Bondel, Ahmed Elnaggar, Saasha Nair, Florian Matthes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationThe Practice of Enterprise Modeling - 11th IFIP WG 8.1. Working Conference, PoEM 2018, Proceedings
EditorsDimitris Karagiannis, Robert Andrei Buchmann, Marite Kirikova
PublisherSpringer Verlag
Pages71-85
Number of pages15
ISBN (Print)9783030023010
DOIs
StatePublished - 2018
Event11th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling, PoEM 2018 - Vienna, Austria
Duration: 31 Oct 20182 Nov 2018

Publication series

NameLecture Notes in Business Information Processing
Volume335
ISSN (Print)1865-1348

Conference

Conference11th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling, PoEM 2018
Country/TerritoryAustria
CityVienna
Period31/10/182/11/18

Keywords

  • EAM
  • Machine learning
  • Software asset management

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