History-based merging of models

Schmidt, Michael; Wenzel, Sven; Kehrer, Timo; Kelter, Udo

doi:10.1109/cvsm.2009.5071716

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…For instance, Gorek and Kelter [25] present means for matching sub-models in an early project phase. Other approaches, e.g., published by Wenzel et al [26] trace model elements through different versions of models, or, published by Schmidt et al [27], apply historybased merging of models.…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Enterprise Architecture Model Evolution

Hacks

Lichter

2018

2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC)

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Enterprise Architecture (EA) is a widely accepted means to ease the alignment of IS projects with enterprisewide objectives. One central artifact of EA are EA models, which provide a holistic view on the organization and support EA's stakeholder to create added value. As EA collects its data from different sources, the data can be contradictory. This work contributes to existing research by proposing a novel approach to deal with contradictory data without solving the thereby caused conflicts. In order to achieve this objective, we refine the Predictive, Probabilistic Architecture Modeling Framework (P 2 AMF) introduced by Johnson et al., which already incorporates a way to represent uncertainty regarding the existence of modelled entities. To make our technique usable, we generalize P 2 AMF from its UML/OCL notation to a graph presentation in order to apply it to EA models notated in arbitrary notations like ArchiMate. Furthermore, we add alternative scenarios in different versions along a time series to meet the requirements of a distributed EA evolution. To show the applicability of our approach, we developed a proof of concept prototype by implementing the proposed calculations and guidelines on a Neo4j graph database. Last, we argue that our approach meets the stated requirements of a distributed EA evolution.

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Section: Related Workmentioning

confidence: 99%

A Probabilistic Enterprise Architecture Model Evolution

Hacks

Lichter

2018

2018 IEEE 22nd International Enterprise Distributed Object Computing Conference (EDOC)

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“…Other operation-based approaches for models have been presented in [20,22], but no dedicated reordering strategies have been discussed. In [21], the authors mention that finding an appropriate sequence for unifying the changes of two parallel change sets is an optimization problem, but they based their approach on manual conflict resolution during the merge process.…”

Section: Related Workmentioning

confidence: 98%

Search-based model merging

Kessentini

Werda

Langer

et al. 2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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In Model-Driven Engineering (MDE) adequate means for collaborative modeling among multiple team members is crucial for large projects. To this end, several approaches exist to identify the operations applied in parallel, to detect conflicts among them, as well as to construct a merged model by incorporating all nonconflicting operations. Conflicts often denote situations where the application of one operation disables the applicability of another operation. Whether one operation disables the other, however, often depends on their application order. To obtain a merged model that maximizes the combined effect of all parallel operations, we propose an automated approach for finding the optimal merging sequence that maximizes the number of successfully applied operations. Therefore, we adapted and used a heuristic search algorithm to explore the huge search space of all possible operation sequences. The validation results on merging various versions of real-world models confirm that our approach finds operation sequences that successfully incorporate a high number of conflicting operations, which are otherwise not reflected in the merge by current approaches.

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“…It is also possible to import own models (Mattsson et al, 1998;Pietruszka, 2014). The user-friendly graphical design of these tools makes it easy to use (Schmidt et al, 2009). Nevertheless, the user needs a good knowledge to build manually the system and use effectively one of these tools.…”

Section: Separating and Combining Modelsmentioning

confidence: 99%

Sequencing of Information in Modular Model-based Systems Design

Rötzer¹,

Rostan²,

Steger³

et al. 2020

DS 103: Proceedings of the 22nd International DSM Conference (DSM 2020), MIT, Cambridge, Massachusetts, October 13th - 15th 202

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Multi-disciplinary design of complex systems is characterized by many inter-dependencies between components. Therefore, adjusting their properties appropriately to satisfy all system requirements is difficult. Simulation models enable fast quantitative assessment of the system behavior. Unfortunately, monolithic system models are often not available, due to domain-specific knowledge that needs to be incorporated. This paper proposes a framework that enables a modular formulation of separate models that can be integrated easily into a full system model. Key ingredient is a model for dependencies between system and component attributes as polyhierarchies without feedback loops. The complex dependency structure of a system can be modularized and simplified. Modular models are integrated into a system model by sequencing the flow of information of the design task. The resulting system model has no circular dependencies and two distinct interfaces: independent design variables and dependent system performance measures. The approach is applied to two gear design tasks.

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History-based merging of models

Cited by 15 publications

References 5 publications

A Probabilistic Enterprise Architecture Model Evolution

A Probabilistic Enterprise Architecture Model Evolution

Search-based model merging

Sequencing of Information in Modular Model-based Systems Design

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