Overview of Data Modelling

Aims of data modelling:

  • Describe what information is contained in database.
  • Describe relationships between data items.
  • Describe constraints on data.

Data modelling is a design process that converts requirements into a data model.

Kinds of data models:

  • Logical: abstract, for conceptual design.
    • Examples: ER, ODL, UML.
  • Physical: record-based for implementation.
    • Examples: relational, SQL.

Quality of Designs

Most important aspects of a design:

  • Correctness: satisfies requirements accurately.
  • Completeness: all requirements covered and assumptions made explicit.
  • Consistency: no contradictory statements.

Potential inadequacies in a design include:

  • Omits information that needs to be included.
  • Contains redundant information (which can lead to inconsistency).
  • Leads to an inefficient implementation.
  • Violates syntactic or semantic rules of data model.