Advances in Model and Data Engineering

Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system.

In the present book, fifteen typical literatures about Model and Data Engineering published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on Model and Data Engineering. We hope this book can demonstrate advances in Model and Data Engineering as well as give references to the researchers, students and other related people.

Components of the Book:
  • Chapter 1
    A global vertical datum defined by the conventional geoid potential and the Earth ellipsoid parameters
  • Chapter 2
    Data mining for software engineering and humans in the loop
  • Chapter 3
    Emergence in the U.S. Science, Technology, Engineering, and Mathematics (STEM) workforce: an agent-based model of worker attrition and group size in high-density STEM organizations
  • Chapter 4
    Validation of evaluation model and evaluation indicators comprised Kansei Engineering and eye movement with EEG: an example of medical nursing bed
  • Chapter 5
    Model-driven interoperability: engineering heterogeneous IoT systems
  • Chapter 6
    Adapting LOD definition to meet BIM uses requirements and data modeling for linear infrastructures projects: using system and requirement engineering
  • Chapter 7
    Lifelong aspect extraction from big data: knowledge engineering
  • Chapter 8
    Incorporating time-delays in S-System model for reverse engineering genetic networks
  • Chapter 9
    Big data services drive mobile crowd embedded opportunistic control mechanism for biological engineering
  • Chapter 10
    Implementing resilience engineering for healthcare quality improvement using the CARE model: a feasibility study protocol
  • Chapter 11
    Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework
  • Chapter 12
    A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models
  • Chapter 13
    Cloud computing for the architecture, engineering & construction sector: requirements, prototype & experience
  • Chapter 14
    Reverse-engineering of gene networks for regulating early blood development from single-cell measurements
  • Chapter 15
    Use, potential, and showstoppers of models in automotive requirements engineering
Readership: Students, academics, teachers and other people attending or interested in Model and Data Engineering
Paul Shapiro, The George Washington University, Washington, US

P. Jaye, Simulation and Interactive Learning (SaIL) Centre, St Thomas’ Hospital, King’s Health Partners, London, UK

Grischa Liebel, Software Engineering Division, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden

Omer F Rana, School of Computer Science & Informatics, Cardiff University, Roath, Cardiff, UK

M. Duncan, Florence Nightingale Faculty of Nursing and Midwifery, King’s College London, London, UK

Simon Baker, University of Cambridge, Cambridge, UK

and more...
This Book

278pp. Published December 2019

Scientific Research Publishing,Inc.,USA

Category:Computer Science & Communications

ISBN: 978-1-61896-814-2

(Hardcover) USD 109.00

ISBN: 978-1-61896-813-5

(Paperback) USD 89.00

Authors/Editors Price: 40% off
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