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Data Modeling Theory and Practice

Book Description

DATA MODELING THEORY AND PRACTICE is for practitioners and academics who have learned the conventions and rules of data modeling and are looking for a deeper understanding of the discipline. The coverage of theory includes a detailed review of the extensive literature on data modeling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice. The practice component incorporates the largest-ever study of data modeling practitioners, involving over 450 participants in interviews, surveys and data modeling tasks. The results challenge many longstanding held assumptions about data modeling and will be of interest to academics and practitioners alike. Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his Data Modeling Essentials a classic in the field. He begins with a question about the nature of data modeling (design or description), and uses it to illuminate such issues as the definition of data modeling, its philosophical underpinnings, inputs and deliverables, the necessary behaviors and skills, the role of creativity, product diversity, quality measures, personal styles, and the differences between experts and novices. Data Modeling Theory and Practice is essential reading for anyone involved in data modeling practice, research, or teaching.

Table of Contents

  1. Chapter 1 Introduction
    1. 1.1. The starting point
    2. 1.2. Conflicting views
    3. 1.3. Importance of the distinction
      1. 1.3.1. Different processes
      2. 1.3.2. Skills and learning
      3. 1.3.3. Multiple solutions
      4. 1.3.4. Are these problems real?
      5. 1.3.5. How much does it matter?
    4. 1.4. Clarifying the question
      1. 1.4.1. Framing the question
      2. 1.4.2. Terminology and scope
      3. 1.4.3. A framework for addressing the description / design question
      4. 1.4.4. Refining the question
    5. 1.5. Overview of research design
    6. 1.6. Organization of the book
  2. Chapter 2 Definitions of design and data modeling
    1. 2.1. Design in information systems
    2. 2.2. Definitions of design
      1. 2.2.1. Lawson’s properties of design
      2. 2.2.2. The antithesis of design
    3. 2.3. Definitions of data modeling
  3. Chapter 3 Beliefs about the database design process
    1. 3.1. A model of the database design process
    2. 3.2. Interaction with the UoD
    3. 3.3. Requirements analysis
    4. 3.4. Conceptual data modeling
      1. 3.4.1. Positions: Description or design?
      2. 3.4.2. Conceptual data modeling languages
      3. 3.4.3. Conceptual data modeling languages – in practice
      4. 3.4.4. The conceptual model as a basis for database design
    5. 3.5. View integration
    6. 3.6. Logical data modeling
    7. 3.7. Physical database design
    8. 3.8. External schema specification
    9. 3.9. Process modeling
    10. 3.10. Beliefs from end-to-end
  4. Chapter 4 Comparison with Lawson’s model
    1. 4.1. Lawson’s characteristics of design
    2. 4.2. Problem
      1. 4.2.1. Problem property 1: Design problems cannot be comprehensively stated
      2. 4.2.2. Problem property 2: Design problems require subjective interpretation
      3. 4.2.3. Problem property 3: Design problems tend to be organized hierarchically
    3. 4.3. Process
      1. 4.3.1. Process property 1: The process is endless
      2. 4.3.2. Process property 2: There is no infallibly correct process
      3. 4.3.3. Process property 3: The process involves finding as well as solving problems (creativity)
      4. 4.3.4. Process property 4: Design inevitably involves subjective value judgments
      5. 4.3.5. Process property 5: Design is a prescriptive activity
      6. 4.3.6. Process property 6: Designers work in the context of a need for action
    4. 4.4. Product
      1. 4.4.1. Product property 1: There is an inexhaustible number of different solutions
      2. 4.4.2. Product property 2: There are no optimal solutions to design problems
      3. 4.4.3. Product property 3: Design solutions are often holistic responses
      4. 4.4.4. Product property 4: Design solutions are a contribution to knowledge
      5. 4.4.5. Product property 5: Design solutions are parts of other design problems
    5. 4.5. Summary and review
  5. Chapter 5 Studies of human factors in data modeling
    1. 5.1. The gold standard
    2. 5.2. Industry participation
    3. 5.3. Problem complexity
    4. 5.4. Focus of the studies
    5. 5.5. Generalizing the findings
    6. 5.6. Summary
  6. Chapter 6 What the thought-leaders think
    1. 6.1. Introduction
    2. 6.2. Design and method
    3. 6.3. Participants
    4. 6.4. Results
      1. 6.4.1. Description vs. design – explicit positions
      2. 6.4.2. Environment
      3. 6.4.3. Problem
      4. 6.4.4. Process
      5. 6.4.5. Product
      6. 6.4.6. Person –modelers and business stakeholders
    5. 6.5. Discussion
  7. Chapter 7 Research design
    1. 7.1. Introduction
    2. 7.2. Research question and sub-questions
    3. 7.3. Research design – overview
      1. 7.3.1. Design dimensions
      2. 7.3.2. How the research components addressed the sub-questions
      3. 7.3.3. Recruitment of participants
      4. 7.3.4. Testing and refining the research design
    4. 7.4. Data collection and management
      1. 7.4.1. Ethical issues
      2. 7.4.2. Administration of questionnaires and tasks
      3. 7.4.3. Demographics and process data
      4. 7.4.4. Data coding and analysis
    5. 7.5. Use of statistics
      1. 7.5.1. Inferential statistics and generalization
      2. 7.5.2. Statistical tests
    6. 7.6. Participants
  8. Chapter 8 Scope and stages
    1. 8.1. Introduction
    2. 8.2. Research design
    3. 8.3. Measures
    4. 8.4. The Scope and Stages questionnaire
    5. 8.5. Method
      1. 8.5.1. Administration of questionnaire
      2. 8.5.2. Basic coding of completed questionnaires
      3. 8.5.3. Coding of stages
      4. 8.5.4. Stage and activities sequence
      5. 8.5.5. Identification of patterns
      6. 8.5.6. Composition of stages
      7. 8.5.7. Classification and responsibility for stages and activities
      8. 8.5.8. Answering the questions
    6. 8.6. Sample
    7. 8.7. Results
      1. 8.7.1. Initial review and coding of the stages
      2. 8.7.2. What are the stages?
      3. 8.7.3. Sequence of stages and activities
      4. 8.7.4. Identification of patterns
      5. 8.7.5. What activities are in each stage?
      6. 8.7.6. Is it data modeling? Who is responsible?
    8. 8.8. Discussion and conclusions
      1. 8.8.1. Answering the questions
      2. 8.8.2. Definition of data modeling
      3. 8.8.3. Comparison with frameworks in the literature
      4. 8.8.4. Observations on the survey instrument and method
  9. Chapter 9 How practitioners describe data modeling
    1. 9.1. Introduction and objectives
    2. 9.2. Research design
    3. 9.3. Method
      1. 9.3.1. Survey administration
      2. 9.3.2. Review and coding of demographics
      3. 9.3.3. Coding of responses to open and closed questions
      4. 9.3.4. Analysis
    4. 9.4. Sample
    5. 9.5. Results
      1. 9.5.1. Coding and descriptive statistics
      2. 9.5.2. Comparison of open question and closed question responses
      3. 9.5.3. Themes
      4. 9.5.4. Correlation with demographics
      5. 9.5.5. Feedback from participants
    6. 9.6. Discussion
      1. 9.6.1. Positions on the description / design question
      2. 9.6.2. Themes
      3. 9.6.3. Observations on research design and method
  10. Chapter 10 Characteristics of data modeling
    1. 10.1. Introduction and objectives
    2. 10.2. Research design
    3. 10.3. Measures
    4. 10.4. The Characteristics of Design questionnaire
    5. 10.5. Method
      1. 10.5.1. Administration of questionnaires
      2. 10.5.2. Recruitment and administration: Architects and accountants
      3. 10.5.3. Analysis of responses
    6. 10.6. Samples
      1. 10.6.1. Data modeling practitioners
      2. 10.6.2. Accountants and architects
    7. 10.7. Results
      1. 10.7.1. Scale reliability
      2. 10.7.2. Overview of results
      3. 10.7.3. Comparison with accountants and architects
      4. 10.7.4. Differences between demographic groups
    8. 10.8. Analysis by property
      1. 10.8.1. Design problems
      2. 10.8.2. The design process
      3. 10.8.3. Design products (solutions)
    9. 10.9. Discussion and conclusions
      1. 10.9.1. Key findings
      2. 10.9.2. Review of research design
  11. Chapter 11 Diversity in conceptual data modeling
    1. 11.1. Objectives and approach
    2. 11.2. Research design
    3. 11.3. Measures
      1. 11.3.1. Assessment of diversity
      2. 11.3.2. Quality measures
    4. 11.4. Materials
      1. 11.4.1. Case study
    5. 11.5. Method
      1. 11.5.1. Administration of laboratory task
      2. 11.5.2. Initial review and coding
      3. 11.5.3. Expert evaluation of quality
      4. 11.5.4. Analysis of diversity
    6. 11.6. Samples
    7. 11.7. Results
      1. 11.7.1. Perceptions of the task and models
      2. 11.7.2. Initial assessment of the models
      3. 11.7.3. Assessment of diversity
    8. 11.8. Coda: The real-world solution
    9. 11.9. Discussion
      1. 11.9.1. Nature of the diversity
      2. 11.9.2. Diversity as a consequence of design
      3. 11.9.3. Alternative explanations for the diversity
      4. 11.9.4. Summary
  12. Chapter 12 Diversity in logical data modeling
    1. 12.1. Objectives and approach
    2. 12.2. Research design
    3. 12.3. Assessment of diversity
    4. 12.4. Materials
    5. 12.5. Method
      1. 12.5.1. Administration of laboratory task
      2. 12.5.2. Initial review and coding
      3. 12.5.3. Analysis of diversity
    6. 12.6. Samples
    7. 12.7. Results
      1. 12.7.1. Process: Perceptions of the task
      2. 12.7.2. Accuracy and completeness of models
      3. 12.7.3. Diversity measures
    8. 12.8. Discussion
      1. 12.8.1. Nature of the diversity
      2. 12.8.2. Source of the diversity
      3. 12.8.3. Alternative explanations for the diversity
      4. 12.8.4. Summary
  13. Chapter 13 Style in data modeling
    1. 13.1. Objectives and approach
    2. 13.2. Research design: An indicator of style
    3. 13.3. Measures
    4. 13.4. Materials
    5. 13.5. Method
      1. 13.5.1. Administration
      2. 13.5.2. Initial review and coding
      3. 13.5.3. Analysis
    6. 13.6. Samples
    7. 13.7. Results
      1. 13.7.1. The Annual Budget model
      2. 13.7.2. Perceptions of the task and models: questionnaire responses
      3. 13.7.3. Initial review and coding
      4. 13.7.4. Analysis
    8. 13.8. Discussion
  14. Chapter 14 Synthesis and conclusions
    1. 14.1. Introduction
    2. 14.2. Answering the sub-questions
      1. 14.2.1. Environment
      2. 14.2.2. Problem
      3. 14.2.3. Process
      4. 14.2.4. Product
      5. 14.2.5. Person
    3. 14.3. An alternative perspective
    4. 14.4. Generalizing the findings
    5. 14.5. Implications
      1. 14.5.1. Implications for data modeling research
      2. 14.5.2. Teaching data modeling
      3. 14.5.3. Data modeling practice
      4. 14.5.4. The integrity of data modeling
    6. 14.6. Research directions