A Machine Learning Based Approach to Application Landscape Documentation

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

doi:10.1007/978-3-030-02302-7_5

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Figure 2 Research Contribution to SAM's Cycle shows that each study has a specific contribution from the SAM cycle's point of view (Nouh, 2016b). The previous researches were on distributing (Sokolova et al, 2017b;Wang et al, 2019), maintaining (Thangamani et al, 2017b), and monitoring (Hubballi & Dogra, 2016b;Landthaler et al, 2018) phase in the SAM cycle.…”

Section: System Softwarementioning

confidence: 99%

Pareto Principle to Improve Anomaly Detection on Software Asset Management

Aripin

Wibowo

2023

SLJIL

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Software Asset Management (SAM) is essential for a large company with a centralized software distribution system. Unfortunately, the operationalization of SAM has various problems. These problems become even more complicated when it comes to managing big data. This research proposes the Pareto Principle to reduce data dimensions to solve the problem of large data sizes without losing dataset characteristics before conducting anomaly detection. This anomaly detection is mandatory to identify and reduce invalid data due to misalignment or misclassification. Therefore, this study compares the state-of-arts anomaly detection algorithms: I-forest, KNN, and SVM. As a result, we found that SVM is the best algorithm, with an accuracy rate of 78.4%. In addition, using Pareto for the total population and software name variation effectively reduces the number of observations to 20% data instances with only 16.5% features without compromising the dataset characteristics. In the best algorithm based on experimental results, the use of Pareto increases accuracy by 3.2% and processing time efficiency by 20%.

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Section: System Softwarementioning

confidence: 99%

Pareto Principle to Improve Anomaly Detection on Software Asset Management

Aripin

Wibowo

2023

SLJIL

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“…Fritscher and Pigneur [81] describe Archimate as a support language for modeling structures within EA. Landthaler et al [82] describe Archimate as standard within the modeling of modern EA. Archimate does not appear in the Gartner hype cycle for EA [66], although Perez-Castillo et al [83] show its high relevance in various modeling applications in the field of EA.…”

Section: Modeling Languages and Eamentioning

confidence: 99%

Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture

Buchkremer

Gampfer

Buchkremer

2021

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As the amount of scientific information increases steadily, it is crucial to improve fast-reading comprehension. To grasp many scientific articles in a short period, artificial intelligence becomes essential. This paper aims to apply artificial intelligence methodologies to examine broad topics such as enterprise architecture in scientific articles. Analyzing abstracts with latent dirichlet allocation or inverse document frequency appears to be more beneficial than exploring full texts. Furthermore, we demonstrate that t-distributed stochastic neighbor embedding is well suited to explore the degree of connectivity to neighboring topics, such as complexity theory. Artificial intelligence produces results that are similar to those obtained by manual reading. Our full-text study confirms enterprise architecture trends such as sustainability and modeling languages.

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“…Further research focuses on the collection of information solely related to the application level. For example, Landthaler et al [41] present, first, a literature review on facilitated methods to automatically collect data for EA model maintenance and, second, propose a new machinelearning-based means for collection. A concrete prototype is implemented by Kleehaus et al [39].…”

Section: Related Workmentioning

confidence: 99%

Implementation of a continuous delivery pipeline for enterprise architecture model evolution

2020

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The discipline of enterprise architecture (EA) is an established approach to model and manage the interaction of business processes and IT in an organization. Thereby, the EA model as a central artifact of EA is subject to a continuous evolution caused by multiple sources of changes. The continuous evolution requires a lot of effort in controlling and managing the evolution of the EA model. This is especially true when merging the induced changes from different sources in the EA model. Additionally, the lack of tool and automation support makes this a very time-consuming and error-prone task. The evolutionary character and the automated quality assessment of artifacts is a well-known challenge in the software development domain as well. To meet these challenges, the discipline of continuous delivery (CD) has emerged to be very useful. The evolution of EA model artifacts shows similarities to the evolution of software artifacts. Therefore, we leveraged practices of CD to practices of EA maintenance. Thus, we created a conceptual framework for automated EA model maintenance. The concepts were realized in a first prototype and were evaluated in a fictitious case study against equivalence classes based on EA model metrics and a set of several requirements for automated EA model maintenance from research. Overall, the concepts prove to be a promising basis for further refinement, implementation, and evaluation in research in an industrial context.

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A Machine Learning Based Approach to Application Landscape Documentation

Cited by 4 publications

References 11 publications

Pareto Principle to Improve Anomaly Detection on Software Asset Management

Pareto Principle to Improve Anomaly Detection on Software Asset Management

Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture

Implementation of a continuous delivery pipeline for enterprise architecture model evolution

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