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2018 | OriginalPaper | Chapter

A Machine Learning Based Approach to Application Landscape Documentation

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

Published in: The Practice of Enterprise Modeling

Publisher: Springer International Publishing

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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.

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Footnotes
1
Flexera FlexNet, https://​www.​flexera.​com, last accessed in November 2017.
 
2
Note that Holm et al. [10] is an extension of Buschle et al. [3].
 
3
In most papers, the authors use an earlier version of the ArchiMate framework, e.g. ArchiMate 2.0. To allow comparability, the EA entities of earlier version versions have been carefully mapped to ArchiMate 3.0.
 
4
Executable binaries identified as Mach-O 64-bit x86_64 executable filetype.
 
5
Keras, v. 2.0.4, https://​keras.​io/​, last accessed in November 2017.
 
7
Scikit-learn, v. 0.19.1, http://​scikit-learn.​org/​stable/​modules/​tree.​html, last accessed in November 2017.
 
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Metadata
Title
A Machine Learning Based Approach to Application Landscape Documentation
Authors
Jörg Landthaler
Ömer Uludağ
Gloria Bondel
Ahmed Elnaggar
Saasha Nair
Florian Matthes
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-02302-7_5

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