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Model Management and Analytics

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.

This book is a result of the 4TU.NIRICT Community Building and is intended for both researchers and practioners who are interested in model-based development and the analytics of large scale models, ranging from Big Data management and analytics to enterprise domains. The book can also be used in graduate courses on model development, data analytics, and data management.

For more info, please refer to the website