Model-based approaches promote the use of models and related artefacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. With their increasing maturity and widespread use (even for large ecosystems and systems of systems), the complexity, size, multiplicity and variety of those artefacts increase. Scalability with respect to variety and multiplicity has remained mostly under the radar.
The initiative MOdel MAnagement And ANalytics (MoMA3N) aims to gather Modelling researchers and practitioners to discuss the emerging scalability problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning.
Topics of Interest (non-exclusive):
Industrial reports and empirical studies on scalability in Modelling
Identification of open research challenges and proposed solutions
Repository mining and management for Modelling artefacts
Clone, pattern, aspect mining for Modelling artefacts
Applications of explorative or descriptive data analytics
Applications of predictive analytics and machine learning
Large-scale model management and consistency checking
Natural language processing for Modelling
Model searching, indexing, retrieval, storage
Visualization of (possibly heterogeneous) large sets of Modelling artefacts
Techniques to analyse and automate (co-)evolution in Modelling
Variability mining and management, model-driven software product lines
Distributed computing for Modelling, with an eye towards Big Data
Intelligent techniques for automating Modelling tasks
01月22日
2018
01月24日
2018
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