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Model Management and Analytics for Large Scale Systems

Langue : Anglais
Couverture de l’ouvrage 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.

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

Bedir Tekinerdogan is a full professor and chair of the Information Technology group at Wageningen University in The Netherlands. He received his MSc degree (1994) and a PhD degree (2000) in Computer Science, both from the University of Twente, The Netherlands. From 2003 until 2008 he was a faculty member at University of Twente, after which he joined Bilkent University until 2015. He has more than 20 years of experience in software engineering research and education. His main research includes the engineering of smart software-intensive systems. In particular, he has focused on and is interested in software architecture design, software product line engineering, model-driven development, parallel computing, cloud computing and system of systems engineering. He has been active in dozens of national and international research and consultancy projects with various large software companies whereby he has worked as a principal researcher and leading software/system architect. He has developed and taught more than 15 different academic software engineering courses and has provided software engineering courses to more than 50 companies in The Netherlands, Germany and Turkey.
Önder Babur is a post-doctoral researcher in the Software Engineering & Technology (SET) group at Eindhoven University of Technology. He holds a PhD from Eindhoven University of Technology, The Netherlands; MSc from RWTH Aachen, Germany and BSc from METU, Turkey. He has further experience as a software engineer in Germany and as a researcher in Spain. His main research interests lie in the fields of model-driven engineering, software architectures, domain-specific languages, and recently applied data mining and machine learning for those domains. Over the years, he has been involved in a number of research projects on automotive software engineering and software product lines, green computing and multiscale modeling for computational science. He currently focuses on data science and machine learni
  • 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