Conceptual data modeling -- capturing the meaning of data from the viewpoint of the user

Concept	Definition
Model	A set of concepts to describe the structure of and operations on a database
Schema	A language or diagrammatic conventions that can be used to provide the modeling construct
Instance	A description of reality at a given point in time

Data modeling tools

1. Entity-relationship diagram (1976 by Peter Chen)
2. Object-oriented diagram

Basic constructs of the E-R model

Concept	Definition	Examples
Entity	A person, place, object, event or concept	Employee, department, building, sale, account
Relationship	An entity that serves to interconnect two or more entity types	Assignment (Employee-Department)
Attribute	A property or characteristic of an entity/relationship type	Employee_name, department_location, sale_date
Constraints	Guiding policies or rules that defines or restricts the structure and processing of a database	All business majors must have a GPA of 2.9 or above

Entity

1. Entity type/set/class - a collection of entities that share common properties, E_i, not mutually disjoint, i.e., e Î E₁, and e Î E₂
2. Entity instance - a single occurrence of an entity type, e
3. Strong entity type - an entity that exists independently of other entity types
4. Weak/ID-dependent entity type - an entity type whose existence depends on some other entity type, e.g., BUILDING-ROOM

Relationship

1. Relationship type - association among entity types (Fig. 3-10a)
2. Relationship instance - association among entity instances (Fig. 3-10b)
3. Identifying relationship - the relationship between a weak entity type and its owner (Fig. 3-5)
4. Gerund/Associative entity - a relationship that is represented as an entity type (Fig. 3-11b)
5. Degree of a relationship - the number of entity types that participate in a relationship
6. Cardinality constraint - specifies the number of instances of one entity that can be associated with each instance of another entity
7. Subtype/Supertype relationship - applying specialization/generalization technique to capture the subset/superset relationship between two entity types (Fig. 4-1)

4 conditions for a gerund

1. Related to the participating entity types in a "many" relationship
2. Identified with a single-attribute identifier
3. Has one or more attributes
4. Participates in one or more relationships beyond the associated relationship

Degree of a relationship

1. Unary/recursive - a relationship between the instances of a single entity type, e.g., Student-Room_With (Fig. 3-12a)
2. Binary - a relationship between the instances of two entity types, e.g, Faculty-Teach-Course (Fig. 3-12b)
3. Ternary - a relationship among the instances of three entity types, e.g., FACULTY-Offer-COURSE-Offer-DEPARTMENT

Cardinality constraints

1. one-to-one, e.g, STUDENT-Assign-PARKING
2. one-to-many, e.g., DEPARTMENT-Offer-COURSE
3. many-to-many, e.g., STUDENT-register-COURSE

Attribute

1. Simple/atomic attribute - an attribute that cannot be broken down into component parts e.g., Student_ID
2. Composite attribute - an attribute that can be broken down into component parts, e.g., Student_Name (First, Middle, Last)
3. Single-valued attribute - an attribute that can take on only one value for a given entity instance, e.g, Student_DateOfBirth
4. Multi-valued attribute - an attribute that may take on more than one value for a given entity instance, e.g., Student_Major
5. Base attribute - an attribute whose values are stored in the database, e.g., Student_Phone
6. Derived attribute - an attribute whose values can be calculated from related attribute values, e.g., Student_GPA
7. Identifier - an attribute (Simple) or combination of attributes (Composite) that uniquely identifies individual entity instance, e.g., Student_ID

Criteria for selecting identifiers

1. Permanent
2. Non-null
3. Non-derived
4. Simple

 Constraints

Business rules (Fig. 4-11)

• Structural - domain constraint, e.g., AGE(e) Î (20, 65) where e Î EMPLOYEE
• Operational - procedural/declarative

Subtype/supertype rules

• Completeness: total/partial specialization rule
• Disjointness; disjoint/overlap rule

Basic E-R notation (Fig. 3-2)

Concept	Symbol
Entity	Rectangle
Weak entity	Double rectangle
Associative entity	Diamond within a rectangle
Relationship	Diamond
Identifying relationship	Double diamond
Cardinality	Crow's foot
Mandatory cardinality	Solid "|"(s) superimposed on the relationship line
Optional cardinality	A "0" superimpose on the relationship line
Subtype/Supertype	Circle
Direction of subtype/supertype	Open-end of the "U" points towards a supertype
Total specialization	Double line extending from a supertype
Disjoint rule	A "d" in the circle joining the supertype & its subtypes
Overlap rule	An "o" in the circle joining the supertype & its subtypes
Attribute	Ellipse
Multi-valued attribute	Double ellipse
Identifier	Underlined
Derived attribute	Broken ellipse

4 steps in designing a conceptual data model using the E-R diagram

• Identify entity sets
• Define the value sets, attributes and primary key for each entity set
• Identify relationship sets and semantic information (cardinality, subtype/supertype) for each relationship set
• Integrate multiple views of entities, attributes, and relationships

Guidelines for identifying entities & attributes

• Entities have descriptive information; identifying attributes do not
• Multivalued attributes should be classified as entities
• If a descriptor in one entity has a many-to-one relationship with another entity, the descriptor should be classified as an entity
• Attach attributes to entities that they describe most directly
• Avoid composite identifiers as much as possible
• If a generalization or subset hierarchy among entities is detected, reattach attributes to the relevant entities

Guidelines for defining relationships

• Eliminate redundant relationships
• A ternary relationship is defined only when the association cannot be represented by several binary relationships among those entities

Which of the following contains a redundant relationship?

• An employee, who lives in a city, belongs to a professional association that can be located in many cities
• An employee work on many projects that are located in various cities in which an employee works.
• A student belongs to many clubs located in a school where the student attends.

Note:  Locate is a redundant relationship 

Which of the following should be defined as a ternary relationship?

• Each student can be involved in many projects and can work under the instruction of several teachers for any of these projects, and each teacher can instruct many students on any project.

• Each student can be involved in several projects and work under the instruction of several teachers, but the student must work under the instruction of exactly one teacher on one project.

• Each employee can be working on any of several projects and using the same notebooks on each project

• Each employee can be working on any of several projects but use exactly one notebook for each project and that notebook belongs only to one employee

Evaluating a conceptual data model

• Flexibility/extensibility -- the ease with which a model can be adapted to changing requirements
• Expressiveness -- the ability to bring out the different abstractions and relationships in a natural way without the need for further explanation
• Simplicity -- easy to use and understand
• Formality -- standardized to provide unique interpretation
• Self-explanatory -- no additional annotations
• Clarity -- no guesswork or ambiguity
• Completeness -- all relevant features of the application domain are represented
• Correctness -- syntactic (concepts are properly defined) & semantic (concepts e.g., entities, relationships, are used according to their definition)
• Minimality -- no concept can be deleted from the schema without losing some information
• Readability -- aesthetically presented (symmetry, minimal crossing & bends, etc.)

Example of an E-R diagram not meeting the expressiveness, clarity and readability criteria:

Example of an E-R diagram not meeting the clarity and readability criteria: