Loek Cleophas – författare

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics.

This book is aimed at both researchers and practitioners 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 could also be used in graduate courses on model development, data analytics and data management.

Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics.This book is aimed at both researchers and practitioners 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 could also be used in graduate courses on model development, data analytics and data management.- Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics- Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics- Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions

This volume constitutes the revised selected papers from the three workshops collocated with the 18th International Conference on Software Engineering and Formal Methods, SEFM 2020, held in Amsterdam, The Netherlands, in September 2020.The 15 full papers presented together with 8 short papers in this volume were carefully reviewed and selected from a total of 35 submissions. The contributions that are collected in this volume have been selected from the presentations at the following workshops:ASYDE 2020: Second International Workshop on Automated and Verifiable Software System Development; CIFMA 2020: Second International Workshop on Cognition: Interdisciplinary Foundations, Models and Applications; and CoSim-CPS 2020: Fourth International Workshop on Formal Co-Simulation of Cyber-Physical Systems.Due to the Corona pandemic this event was held virtually.