Model Management and Analytics for Large Scale Systems

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 and managing data. With their increased widespread 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 the complex data analysis processes carried out by scientists in their day-to-day work. The authors aim to tackle this problem and set the agenda for novel approaches in large scale model management and 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. Academics working in the field of data science will gain novel insights in the topic of model analytics that go beyond both model-based development and data analytics. The book could also be used in graduate courses on model development, data analytics, and data management.

• Identifies key problems and offers solution approaches as well as tools which have been developed or are necessary for model management and analytics
• Explores the basic theory and background, the current research topics, the related challenges, and the research directions for the 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, as well as important future directions