Enterprise Architecture Planning: Analyses of Requirements from Practice and Research

Nowakowski, Emmanuel; Farwick, Matthias; Trojer, Thomas; Häusler, Martin; Kessler, Johannes; Breu, Ruth

doi:10.24251/hicss.2017.589

Cited by 12 publications

(21 citation statements)

References 24 publications

(27 reference statements)

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“…Enterprise architecture metamodels are often used to run different simulations and analyses [22], [23], which requires granular and up-to-date information [24]. Up to date information is critical for the success of enterprise transformation projects [8].…”

Section: Automated and Multi-source Modelingmentioning

confidence: 99%

“…The respondents prioritized correct data granularity, consistency, and being able to identify the source of data. Nowakowski et al [8] found in their enterprise architecture management requirements study that optimal tools need to support heterogeneous data because enterprise data are available in different forms and spread over multiple repositories.…”

Section: Automated and Multi-source Modelingmentioning

confidence: 99%

“…To create more complete models additional data sources are needed. The need for multiple data sources for enterprise system modeling has been mentioned by multiple authors such as [5], [6], [7], [8]. Thus, modeling automation would benefit from using multiple data sources.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

Välja

Lagerström

Franke

et al. 2019

CSIMQ

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Modeling IT architecture is a complex, time consuming, and error prone task. However, many systems produce information that can be used for automating modeling. Early studies show that this is a feasible approach if we can overcome certain obstacles. Often more than one source is needed in order to cover the data requirements of an IT architecture model; and the use of multiple sources means that heterogeneous data needs to be merged. Moreover, the same collection of data might be useful for creating more than one kind of models for decision support. IT architecture is constantly changing and data sources provide information that can deviate from reality to some degree. There can be problems with varying accuracy (e.g. actuality and coverage), representation (e.g. data syntax and file format), or inconsistent semantics. Thus, integration of heterogeneous data from different sources needs to handle data quality problems of the sources. This can be done by using probabilistic models. In the field of truth discovery, these models have been developed to track data source trustworthiness in order to help solving conflicts while making quality issues manageable for automatic modeling. We build upon previous research in modeling automation and propose a framework for merging data from multiple sources with a truth discovery algorithm to create multiple IT architecture models. The usefulness of the proposed framework is demonstrated in a study where models using three tools are created, namely; Archi, securiCAD, and EMFTA.

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Section: Automated and Multi-source Modelingmentioning

confidence: 99%

Section: Automated and Multi-source Modelingmentioning

confidence: 99%

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A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

Välja

Lagerström

Franke

et al. 2019

CSIMQ

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“…However, research indicates low use of EAs for decision-making in organizations [1,15,22], in particular for visualizing and, hence, understanding complex IT landscapes [8,27,46]. Potential reasons for this include the limited perceived usefulness of EA visualizations, which are often characterized by their complexity [32], lack of focus [8], an inappropriate level of abstraction [27,46], or insufficient tool support [27]. In sum, this inhibits the effective use of EAs for decision-making [6], so that stakeholders often find the added value of EA visualizations to be rather low [15,32].…”

Section: Introductionmentioning

confidence: 99%

Let’s Get in Touch - Decision Making about Enterprise Architecture Using 3D Visualization in Augmented Reality

Rehring¹,

Greulich²,

Bredenfeld³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Making informed decisions about historically grown and often complex business and Information Technology (IT) landscapes can be particularly difficult. Enterprise Architecture Management (EAM) addresses this issue by enabling stakeholders to base their decisions on relevant information about the organization's current and future Enterprise Architectures (EAs). However, visualization of EA is often confronted with low usefulness perceptions. Informed by the cognitive fit theory (CFT), we argue that decision-makers benefit from interacting with EA visualizations using Augmented Reality (AR), because it enables a consistent task-related mental representation based on the natural use of decision-makers' visualspatial abilities. The goal of this paper is to demonstrate ARs suitability for EA-related decision-making. We follow the design science research (DSR) approach to develop and evaluate an AR head-mounted display (HMD) prototype, using the Microsoft HoloLens. Our results suggest that EA-related decision-making can profit from applying AR, but users find the handling of the HMD device cumbersome.

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“…The absence of a reliable and up-todate documentation can result in slow error detection, loss of traceability of changes and misguided planning processes due to poor information situations. Ultimately, these issues can lead to problems which cause very high costs for the companies if they remain unaddressed [30,51].…”

mentioning

confidence: 99%

ChronoSphere: a graph-based EMF model repository for IT landscape models

et al. 2019

Self Cite

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IT Landscape models are representing the real-world IT infrastructure of a company. They include hardware assets such as physical servers and storage media, as well as virtual components like clusters, virtual machines and applications. These models are a critical source of information in numerous tasks, including planning, error detection and impact analysis. The responsible stakeholders often struggle to keep such a large and densely connected model up-to-date due to its inherent size and complexity, as well as due to the lack of proper tool support. Even though modeling techniques are very suitable for this domain, existing tools do not offer the required features, scalability or flexibility. In order to solve these challenges and meet the requirements that arise from this application domain, we combine domain-driven modeling concepts with scalable graphbased repository technology and a custom language for model-level queries. We analyze in detail how we synthesized these requirements from the application domain and how they relate to the features of our repository. We discuss the architecture of our solution which comprises the entire data management stack, including transactions, queries, versioned persistence and metamodel evolution. Finally, we evaluate our approach in a case study where our open-source repository implementation is employed in a production environment in an industrial context, as well as in a comparative benchmark with an existing state-of-the-art solution. Keywords Model-driven engineering • Model repositories • Versioning • Graph database • IT landscape 1 Introduction Model-driven engineering (MDE) is a discipline that aims at improving the processes, workflows and products of exist-Communicated by Dr. Ana Moreira.

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Enterprise Architecture Planning: Analyses of Requirements from Practice and Research

Abstract: Enterprise architecture management (EAM)

Cited by 12 publications

References 24 publications

A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

Let’s Get in Touch - Decision Making about Enterprise Architecture Using 3D Visualization in Augmented Reality

ChronoSphere: a graph-based EMF model repository for IT landscape models

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