Model Management and Analytics for Large Scale Systems

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 analyticsExplores basic theory and background, current research topics, related challenges and the research directions for model management and analyticsProvides 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

Part 1. Concepts and challenges

1. Introduction to modelmanagement and analytics

2. Challenges and directions for a community infrastructure for Big Data-driven research in software architecture

3. Model clone detection and its role in emergent model pattern mining

4. Domain-driven analysis of architecture reconstruction methods

Part 2. Methods and tools

5. Monitoring model analytics over large repositories with Hawk and MEASURE

6. Model analytics for defect prediction based on design-level metrics and sampling techniques

7. Structuring large models with MONO: Notations, templates, and case studies

8. Delta-oriented development of model-based software product lines with DeltaEcore and SiPL: A comparison

9. OptML framework and its application tomodel optimization

Part 3. Industrial applications

10. Reducing design time and promoting evolvability using Domain-Specific Languages in an industrial context

11. Model analytics for industrialMDE ecosystems