1. 1 A Demo of Logical Database Design

2. 2 Aim of the demo To develop an understanding of the logical view of data and the importance of the relational model To consider entities, attributes, and tables within the relational view Learn basic and advanced ER modelling techniques

3. 3 Entities Relational database design starts by defining the required entities Entities are anything you want to store data about –people (students, customers, employees, etc.) –places (resorts, cities, countries, etc.) –things (restaurants, products, invoices, movies, paintings, books, buildings, contracts, etc.) –events (elections, presentations, earthquakes, hurricanes, etc.)

4. 4 Entity sets are collections of related entities. Entities are related by their classification: student entities are related by the fact that they are all students invoice entities are related by the fact that they are all invoices car entities are related by the fact that they are all cars Entity Sets / Entity Types

5. 5 Unfortunately, database designers almost always use the two terms as synonyms. When database designers refer to entities, they really are referring to entity sets or entity types. Therefore, when you see a reference to an EMPLOYEE “entity” in a database design, remember that EMPLOYEE actually represents an entity set/type that contains a collection of employee entities and each instance of this type is one single entity. Entity Set (type) = Class Entity = Object Entity Sets/Types and Entities

6. 6 Attributes Properties of entities or relationships This is the data stored about an entity  E.g. student entity could have attributes such as name, address, DOB etc.

7. 7 Types of attributes Simple – each entity has a single atomic value for the attribute. Cannot be subdivided –Age, sex, marital status Composite (composed of several components): Can be subdivided into additional attributes –Address into street, city, zip –Name(FirstName, MiddleName, LastName) Single-valued: Can have only a single value –Person has one social security number Multi-valued: an entity may have multiple values for that attribute –Person may have several college degrees (qualifications, skills) –Color of a car Derived: Can be derived with algorithm –Age can be derived from date of birth

8. 8 Conceptual design: (ER Model is used at this stage.) – What are the entities and relationships in the database? – What information about these entities and relationships should we store in the database? – What are the integrity constraints or business rules that hold? – A database `schema’ in the ER Model can be represented pictorially (ER diagrams). – Can map an ER diagram into a relational schema by hands or tools. Overview of Database Design

9. 9 ER Diagram: Two Most Commonly Used Notations

10. 10 ER Diagrams - The Basics

11. 11 ER Diagram Basics - Cardinalities

12. 12 Classification of Cardinalities Minimum cardinality - relationship participation –Mandatory: (existence dependent) Entity occurrence requires corresponding occurrence in the related entity. Offering cannot exist without being related to a course. –Optional: Entity occurrence does not require a corresponding occurrence in the related entity. A course does not necessarily need an offering to exist. Maximum cardinality based –1-M –M-N –1-1

13. 13 Weak Entities – Identifying Relationship Identifying relationship –One entity is existence-dependent on another –PK of related entity contains PK component of parent entity

14. 14 –A weak entity is an entity that Is existence-dependent i.e. weak entities cannot exist without the owner entity AND Has a primary key that is partially or totally derived from the parent entity in the relationship. They do not have the PK of their own –However not every existence dependency results in a weak entity type. Eg – Driver License cannot exist unless it is related to PERSON entity even though it has its own key (Licence number) and hence is not a weak entity. Weak Entities

15. 15 M-N Relationships with Attributes Replace M-N relationship with Associative entity type Two identifying 1-M relationships

16. 16 Associative Entity Type Example

17. 17 – A recursive entity is one in which a relationship can exist between occurrences of the same entity set. – A recursive entity is found within a unary relationship. Recursive Entities

18. 18 Entity Supertypes and Subtypes Generalization hierarchy – Depicts relationships between higher-level supertype and lower-level subtype entities – Supertype has shared attributes – Subtypes have unique attributes – A subtype entity inherits its attributes and its relationships from the supertype entity – The supertype and its subtype(s) maintain an is-a relationship

19. 19 Generalization Hierarchies

20. 20 Disjointness and Completeness of Subtypes Disjointness: D means intersection is empty Overlap: No symbol means intersection is not empty: Faculty and Student. Subtypes have common entities Completeness: C means union of subtype entities is the set of supertype entities Nothing: supertype can have free standing entities ((not in any subtype). Union does not provide supertype

21. 21 Entity Vs Attribute Should address be an attribute of Employees or an entity (connected to Employees by a relationship)? Depends upon the use we want to make of address information, and the semantics of the data: If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued). If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modelled as an entity (since attribute values are atomic).

22. 22 Summary A data model is the relatively simple representation, usually graphic, of complex real-world data structures. It represents data structures and their characteristics, relations, constraints, and transformations. Different type attributes have different implications in a database. Some of these need to be considered carefully.

23. 23 Comprehensive Example