Module 6 - Data Modeling

Data Modeling Overview

• Definition:

  • A technique for modeling data determining what data and relationships should be stored in a database.

  • Graphical representation of a database, effectively communicating a database design.

• Goals:

  • Identify the facts to be stored in the database.

  • Requires collaboration between client and analyst.

• Process:

  • Iterative, involving trial and revision.

  • The data model serves as a working document.

Building Blocks of Data Modeling

• Entity:

  • A thing about which data is stored; the basic building block.

• Attribute:

  • Describes an entity; singular and unique within the model.

• Relationship:

  • Describes the linkage between two entities, defined by relationship descriptors:

    • 1:1 (one-to-one)

    • 1:m (one-to-many)

    • m:m (many-to-many)

• Identifier:

  • Uniquely distinguishes an instance of an entity.

Quality of Data Models

• Well-formed Data Model:

  • All construction rules are obeyed.

  • No ambiguity is present.

  • All entities, attributes, and relationships are defined.

  • Names are meaningful and understood by the client.

• High Fidelity Image:

  • Accurately describes the real-world it represents.

  • Relationships are correctly established.

  • Completeness and understandability are key.

  • The model must make sense to the client.

Quality Improvement Considerations

• Assess:

  • Level of detail of the model.

  • All exceptions must be handled.

  • Overall accuracy of the model.

Modality & Cardinality

• Modality:

  • Also known as optionality; defines the minimum number of instances in a relationship.

• Cardinality:

  • Indicates the range of instances in a relationship.

  • Links with modality.

• Cardinality Examples:

  • 0,1: Optional (zero or one instances)

  • 0,n: Zero or many instances

  • 1,1: Mandatory (exactly one instance)

Types of Entities

• Independent:

  • Can stand alone; often prominent in the client's mind.

• Dependent:

  • Relies on another entity for existence and identification.

  • Can become independent with an arbitrary identifier.

• Associative:

  • Results from m:m relationships; holds current or historical data.

• Aggregate:

  • Formed from multiple entities with a common prefix or suffix.

• Subordinate:

  • Entity data that varies among instances.

Generalization

• Describes a relationship between more general and more specific elements.

  • For each subtype, the primary key must represent the supertype's key as a foreign key.

UML Aggregation Concepts

• Aggregation:

  • A part-whole relationship.

  • Shared Aggregation:

    • Enables multiple entities to own the same entity.

  • Composite Aggregation:

    • One entity exclusively owns the other.

Hints on Data Modeling

• The model may expand and contract; include identifiers when necessary.

• Ensure identifiers have a singular purpose—identification.

• Create attributes when instance ordering is required.

• Choose names carefully; be aware of synonyms and homonyms.

• Maintain clarity by labeling relationships and ensuring data model accuracy.

Meaningful vs Non-Meaningful Identifiers

• Meaningful Identifiers:

  • Draw inferences about entity attributes; can be recognizable.

  • Advantages: Easy to remember;

  • Disadvantages: Can lead to identifier exhaustion as realities change.

• Non-Meaningful Identifiers:

  • Serve the sole purpose of uniquely identifying the entity; avoid data management issues.

  • Attributes describe characteristics of the entity.

Key Takeaways

• A high-fidelity data model handles all exceptions.

• Identifiers only need to distinguish an instance.

• Data modeling skills develop over time.