A situational method for semi-automated Enterprise Architecture Documentation

Farwick, Matthias; Schweda, Christian M.; Breu, Ruth; Hanschke, Inge

doi:10.1007/s10270-014-0407-3

Cited by 35 publications

(20 citation statements)

References 23 publications

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“…There have been attempts to generate models automatically from available data. Farwick et al [28] propose a semi automated way of maintaining enterprise architecture models using technical interfaces and humans. The technical interfaces are assumed to be web services and the data used should be in a machine readable format.…”

Section: Automated and Multi-source Modelingmentioning

confidence: 99%

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%

A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

Välja

Lagerström

Franke

et al. 2019

CSIMQ

View full text Add to dashboard Cite

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“…Farwick et al [9] developed a method for automated EA documentation. The method included four techniques [9]: (1) task-based reminders, (2) automated structured data collection, (3) external event triggers, and (4) internal model event triggers. Among these techniques, three of them (1, 3, and 4) notified the users that an EA model needed to be revised.…”

Section: Automated Enterprise Architecture Documentation and Modelingmentioning

confidence: 99%

“…Therefore, the existing automated methods do not adequately address the industrial challenges. Moreover, no method has addressed the challenge of abstracting the excessively detailed data from ESs to be relevant for EA models [6], [9]. In addition, explanations of the existing automated methods are insufficient to allow them to be replicated.…”

Section: Introductionmentioning

confidence: 99%

Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA): Business Process Model Automation in an Industry 4.0 Laboratory

Nardello

Han

Møller

et al. 2019

CSIMQ

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The transformation towards the Industry 4.0 paradigm requires companies to manage large amounts of data. This poses serious challenges with regard to how effectively to handle data and extract value from it. The state-ofthe-art research of Enterprise Architecture (EA) provides limited knowledge on addressing this challenge. In this article, the Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA) method is proposed and demonstrated. An abstraction hierarchy is introduced by AMA4EA to support companies to automatically abstract data from enterprise systems to concepts, then to automatically create an EA model. AMA4EA was demonstrated at an Industry 4.0 laboratory. The demonstration showed that AMA4EA could abstract detailed data from the Enterprise Resource Planning (ERP) system and Manufacturing Execution System (MES) to be relevant for a business process model that provided a useful and simplified visualization of production process data. The model communicated the detailed business data in an easily understandable way to stakeholders. AMA4EA is an innovative and novel method that contributes new knowledge to EA research. The demonstration provides sufficient evidence that AMA4EA is useful and applicable in the Industry 4.0 environment.

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“…These maintenance processes require the active involvement of EA stakeholders and data owners that provide the information from federated models and revise inconsistencies during the integration with the EA meta-model [18]. While research recently promotes technical systems that attempt to support stakeholders of FEAMM with the automated provisioning of aggregated information from existing specialized models [14], [31], the enterprise-wide stakeholder engagement for sharing local data and contributing to an enterprise-wide EA model remains a major challenge. The active engagement of stakeholders during the creation and maintenance processes requires an institutionalization of EA in the organization.…”

Section: Motivationmentioning

confidence: 99%

“…Our findings summarize novel and integrated success factors that are crucial for the establishment of a successful FEAMM in order to maintain a solid and up to date model that can serve as a foundation for successful EA management. While existing research mainly investigates technology to automate the provision of EA information from existing specialized models in the organization [14] [29], this paper considers the socio-technical perspective in which FEAMM is embedded.…”

Section: Motivationmentioning

confidence: 99%

Success Factors for Federated Enterprise Architecture Model Management

Khosroshahi

Aier

Hauder

et al. 2015

Lecture Notes in Business Information Processing

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Recent approaches for managing Enterprise Architecture (EA) models provide technical systems to procure information from existing repositories within the application landscape of an organization. Beyond technical solutions, social factors are of utmost importance to implement a successful EA initiative. Institutional theory has for example been employed to understand crucial factors for realizing EA management (EAM) benefits through architectural thinking. Yet, it remains unclear how these social factors influence a federated approach for EA model management. Based on a socio-technical systems perspective, we investigate success factors for Federated EA Model Management (FEAMM) by conducting qualitative interviews with industry experts. Our findings suggest that success factors for FEAMM are related to the model sources, modeling instruments, and model integration aspects from a technical perspective as well as to organizational grounding, governance, enforcement, efficiency, goal alignment, and trust from a social perspective.

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A situational method for semi-automated Enterprise Architecture Documentation

Cited by 35 publications

References 23 publications

A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery

Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA): Business Process Model Automation in an Industry 4.0 Laboratory

Success Factors for Federated Enterprise Architecture Model Management

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