Model Management and Analytics for Large Scale Systems

Book description

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

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Analysis in the large: A foreword
  7. Preface
    1. Introduction
    2. Why a book on model management and analytics
    3. Book outline
  8. Part 1: Concepts and challenges
    1. Chapter 1: Introduction to model management and analytics
      1. Abstract
      2. 1.1. Introduction
      3. 1.2. Data analytics concepts
      4. 1.3. The inflation of modeling artifacts
      5. 1.4. Relevant domains for MMA
      6. References
    2. Chapter 2: Challenges and directions for a community infrastructure for Big Data-driven research in software architecture
      1. Abstract
      2. 2.1. Introduction
      3. 2.2. Related work
      4. 2.3. Experiences in creating & sharing a collection of UML software design models
      5. 2.4. Challenges for Big Data-driven empirical studies in software architecture
      6. 2.5. Directions for a community infrastructure for Big Data-driven empirical research in software architecture
      7. 2.6. Overview of CoSARI
      8. 2.7. Summary and conclusions
      9. References
    3. Chapter 3: Model clone detection and its role in emergent model pattern mining
      1. Abstract
      2. 3.1. Introduction
      3. 3.2. Background material
      4. 3.3. MCPM – a conceptual framework for using model clone detection for pattern mining
      5. 3.4. Summary of challenges and future directions
      6. 3.5. Conclusion
      7. References
    4. Chapter 4: Domain-driven analysis of architecture reconstruction methods
      1. Abstract
      2. 4.1. Introduction
      3. 4.2. Preliminaries
      4. 4.3. Domain model of architecture reconstruction methods
      5. 4.4. Concrete architecture reconstruction method
      6. 4.5. Related work
      7. 4.6. Discussion
      8. 4.7. Conclusion
      9. Appendix 4.A. Primary studies
      10. References
  9. Part 2: Methods and tools
    1. Chapter 5: Monitoring model analytics over large repositories with Hawk and MEASURE
      1. Abstract
      2. Acknowledgements
      3. 5.1. Introduction
      4. 5.2. Motivation
      5. 5.3. Background
      6. 5.4. Monitoring model analytics over large repositories with Hawk and MEASURE
      7. 5.5. Case study: the DataBio models
      8. 5.6. Related projects
      9. 5.7. Conclusions
      10. Appendix 5.A. Running example
      11. Appendix 5.B. EOL-based ArchiMate metric implementation
      12. References
    2. Chapter 6: Model analytics for defect prediction based on design-level metrics and sampling techniques
      1. Abstract
      2. 6.1. Introduction
      3. 6.2. Background and related work
      4. 6.3. Methodology
      5. 6.4. Experimental results
      6. 6.5. Discussion
      7. 6.6. Conclusion
      8. References
    3. Chapter 7: Structuring large models with MONO: Notations, templates, and case studies
      1. Abstract
      2. 7.1. Introduction
      3. 7.2. Modeling in the large
      4. 7.3. Structuring big models
      5. 7.4. Describing and specifying model structures
      6. 7.5. Case study 1: Library Management System (LMS)
      7. 7.6. Case study 2: BIENE Erhebung (ERH)
      8. 7.7. Discussion
      9. 7.8. Conclusions
      10. References
    4. Chapter 8: Delta-oriented development of model-based software product lines with DeltaEcore and SiPL: A comparison
      1. Abstract
      2. 8.1. Introduction
      3. 8.2. Running example
      4. 8.3. Delta modeling for MBSPLs
      5. 8.4. Delta modeling with DeltaEcore and SiPL
      6. 8.5. Capabilities of DeltaEcore and SiPL
      7. 8.6. Related work
      8. 8.7. Conclusion
      9. References
    5. Chapter 9: OptML framework and its application to model optimization
      1. Abstract
      2. 9.1. Introduction
      3. 9.2. Illustrative example, problem statement, and requirements
      4. 9.3. The architecture of the framework
      5. 9.4. Examples of models for registration systems based on various architectural views
      6. 9.5. Model processing subsystem
      7. 9.6. Model optimization subsystem
      8. 9.7. Related work
      9. 9.8. Evaluation
      10. 9.9. Conclusion
      11. Appendix 9.A. Feature model
      12. Appendix 9.B. Platform model
      13. Appendix 9.C. Process model
      14. Appendix 9.D. The instantiation of the value metamodel for energy consumption and computation accuracy
      15. References
  10. Part 3: Industrial applications
    1. Chapter 10: Reducing design time and promoting evolvability using Domain-Specific Languages in an industrial context
      1. Abstract
      2. 10.1. Introduction
      3. 10.2. Domain-Specific Languages
      4. 10.3. State of the art
      5. 10.4. Approach to practical investigation
      6. 10.5. DSL ecosystem design
      7. 10.6. Results of practical investigation
      8. 10.7. Evaluation
      9. 10.8. Conclusions
      10. References
    2. Chapter 11: Model analytics for industrial MDE ecosystems
      1. Abstract
      2. 11.1. Introduction
      3. 11.2. Objectives
      4. 11.3. Background: SAMOS model analytics framework
      5. 11.4. MDE ecosystems at ASML
      6. 11.5. Model clones: concept and classification
      7. 11.6. Using and extending SAMOS for ASOME models
      8. 11.7. Case studies with ASML MDE ecosystems
      9. 11.8. Discussion
      10. 11.9. Related work
      11. 11.10. Conclusion and future work
      12. References
  11. Index

Product information

  • Title: Model Management and Analytics for Large Scale Systems
  • Author(s): Bedir Tekinerdogan, Onder Babur, Loek Cleophas, Mark van den Brand, Mehmet Aksit
  • Release date: September 2019
  • Publisher(s): Academic Press
  • ISBN: 9780128166505