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Data Modelling


Data modeling in software engineering is the process of creating a data model for an information system by applying formal data modeling 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.
  • A basic knowledge of data modeling concepts and techniques is required.
  • It is a 12 days program and extends up to 2hrs each.
  • The format is 40% theory, 60% Hands-on.

  • It is a 3 days program and extends up to 8hrs each.
  • The format is 40% theory, 60% Hands-on.
    Private Classroom arranged on request and minimum attendies for batch is 4.
course content
  • Introduction to Logical Data Modeling
    • Definitions
    • Benefits of logical data modeling
    • Data modeling vs. physical database design
    • Roles involved in data modeling
    • Steps in the data modeling process
    • Example data model
  • Entities
    • Identifying entities
    • Validating entities
    • Documenting "instances" of entities
    • Distinguishing entities from attributes
    • Naming entities
    • Starting an Entity/Relationship (E/R) diagram
  • Relationships
    • Identifying significant relationships
    • Determining the "cardinality" or "degree" of a relationship
    • One-to-One
    • One-to-Many
    • Many-to-Many
    • Determining whether a relationship is optional or mandatory
    • Giving a relationship a name
    • Documenting the relationships in the E/R diagram
    • Walking people through an E/R diagram
    • Resolving Many-to-Many Relationships
    • Real-world examples of many-to-many relationships
    • Why many-to-many relationships are broken down into simpler relationships
    • Identifying "association" or "intersection" entities
    • Documenting the new relationships in the E/R diagram
  • Attributes and Normalization
    • Defining and categorizing attributes
    • Domains and integrity rules
    • Unique identifiers/primary keys
    • Foreign keys
    • Occurrence population
    • Normalization: validating the placement of each attribute
    • Attribute does not repeat (first normal form)
    • Attribute is dependent on its entire UID (second normal form)
    • Attribute is dependent only on its UID (third normal form)
  • Subtypes and Supertypes
    • Identifying subtypes: real-world examples of subtypes and supertypes
    • Determining when entities are similar
    • UIDs
    • Attributes
    • One-to-one relationships
    • Creating subtypes and supertypes
    • "Type" entities
    • Using subtypes to apply fourth normal form
    • Establishing the relationships of the sub- and super-entities to other entities
    • Mutually exclusive vs. non-mutually exclusive subtypes
    • "Role" entities to handle complex subtypes
  • Recursive Relationships
    • Real-world examples of recursive relationships
    • Discovering recursive relationships
    • Determining whether the relationships are optional or mandatory
    • Documenting the new relationships in the E/R diagram
    • Hierarchical vs. Network recursive relationships
    • "Structure" or "Bill of Materials" entities: fifth normal form
  • Implementing a Relational Database
    • Relational database objects: tables, views, indexes, etc.
    • Mapping logical objects to physical objects
    • Denormalization
      • Why
      • How
      • Pros/Cons
    • Distributing databases
    • Referential integrity
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