Concepts, Architecture and Conceptual Design (ER Model)

We learned that databases are useful because:

• They store a lot of data for applications.
• Give desirable guarantees for transactions.
• Are efficient and reliable.
• Many more reasons..

Before any of this is possible, we need to carefully design a database.

CLO2: Develop conceptual modeling concepts and notations using Entity-Relationship diagrams to analyze and design complex database applications. (Cognitive Skill) Chapter objectives:

• Define some basic concepts related to databases (abstraction, schemas, and instances).
• Explain the main components of ER diagrams (entities, attributes, and relationships).
• Given a description of a problem, create an ER diagram. Justify your choice of entities, relationships, keys, cardinality constraints, participation constraints, and weak entities.

Database systems provide an abstract view of the data.

The 3 levels of abstraction are as follows:

• Physical level: how data are stored
• Logical level: what data are stored
• External level (views): Shows different parts of the db to different users
  • Example: Student view is different from registrar or database admin.
• We refer to the suppression of data storage details as data abstraction.
• Data abstraction is one of the key advantages of using relational databases.

Data Independence refers to the protection of user applications to changes made in the definition and organization of data.

Application programs should not, ideally, be exposed to details of data representation and storage. The DBMS provides an abstract view of the data that hides such details.

There are two types of data independence: physical and logical data independence.

Logical Data Independence: The capacity to change the conceptual schema without having to change the external schemas and their associated application programs.

Logical Data independence means if we add some new columns or remove some columns from table then the user view and programs should not change.

 For example: consider two users A & B. Both are selecting the fields "EmployeeNumber" and

"EmployeeName". If user B adds a new column (e.g. salary) to his table, it will not affect the external view for user A, though the internal schema of the database has been changed for both users A & B.

Physical Data Independence: The capacity to change the internal schema without having to change the conceptual schema.



Modifications at the physical level are occasionally necessary to improve performance. For example, a change to the internal schema, such as using different file organization or storage structures, storage devices, or indexing strategy, should be possible without having to change the conceptual or external schemas.

Logical data independence is more difficult to achieve than physical data independence, since application programs are heavily dependent on the logical structure of the data that they access.

We have our enterprise we intend to model. What are the entities and relationships in this enterprise?

Entities: (usually) nouns that represent things in the enterprise (e.g. Student, course, etc) Relationships: (usually) verbs that represent statements about 2 or more objects (e.g. Student takes course).

What information should we store about those entities and relationships?

What rules do we need to make sure our data is sound?

Integrity constraints.

The encoding in the relational database realm is referred to as an Entity-Relationship (ER) diagram.

Entity: Real world object distinguishable from other objects.

attributes describe an entity.

A set of Entity set: A collection of similar entities. E.g. Employee is an entity set for all employees.

• All entities in an entity set have the same set of attributes.
• Each attribute has a domain (e.g. float, int, date).
• Each entity set has a key attribute.

Simple  Each entity has a single atomic value for the attribute. For

example, SSN or Sex.

Composite  The attribute may be divided into smaller parts. For example:

 Address(Number, Street, City, Country), or  Name(FirstName, MiddleName, LastName).

 Composition may form a hierarchy where some components are

themselves composite.

Multi-valued attributes:

• A set of values for the same entity.
  • E.g. colors attribute for a car - {Color} 1 3 ...
  • College degrees attribute for a person - {college degree}.1 3 ...
  • A multi-valued attribute may have lower and upper bounds to constrain the number of values allowed e.g. the colors attribute of a car may have between 1 and 3 values.

• When two or more attributes are related, the value of one attribute can be obtained from the other.
• E.g. The age can be determined from the current date and the value of the birth date of a person, so age is a derived attribute and birthdate is a stored attribute.
• Some attribute values can be derived from related entities; e.g. an attribute Number_of_employees of a DEPARTMENT entity can be derived by counting the number of employees related to that department. BDate Age

In general, composite and multi-valued attributes may be nested arbitrarily to any number of levels, although this is rare.

 For example, PreviousDegrees of a STUDENT is a composite

multi-valued attribute denoted by {PreviousDegrees (College, Year, Degree, Field)}

 Multiple PreviousDegrees values can exist  Each has four subcomponent attributes:

 College, Year, Degree, Field

This one is very rare and unlikely to occur. It is also not a good design technique as we will see later.

Entities with the same basic attributes are grouped or typed into an entity type.

 For example, the entity type EMPLOYEE and PROJECT.

An attribute of an entity type for which each entity must have a

key attribute of the entity type.

unique value is called a  For example, SSN of EMPLOYEE or student ID for a student.

A key attribute may be composite.

 VehicleTagNumber is a key of the CAR entity type with components

(Number, State).

An entity type may have more than one key.

 The CAR entity type may have two keys:

 VehicleIdentificationNumber (popularly called VIN)  VehicleTagNumber (Number, State), aka license plate number.

Each key is underlined (Note: this is different from the relational schema where only one “primary key” is underlined).

In ER diagrams, an entity type is displayed in a

rectangular box

Attributes are displayed in

ovals

 Each attribute is connected to its entity type  Components of a composite attribute are connected to the oval representing

the composite attribute

 Each key attribute is underlined  Multivalued attributes displayed in double ovals See the full ER notation in advance on the next slide

We need to create a database schema design based on the following (simplified)

requirements of the COMPANY Database:

 The company is organized into DEPARTMENTs. Each department has a name,

manages the department. We keep track of

number and an employee who the start date of the department manager. A department may have several locations.

controls a number of PROJECTs. Each project has a unique

 Each department

name, unique number and is located at a single location.

 The database will store each EMPLOYEE’s social security number, address,

salary, sex, and birthdate.

works for one department but may

work on several

 Each employee

projects.

 The DB will keep track of the number of hours per week that an employee

currently works on each project.

 It is required to keep track of the

direct supervisor of each employee.

have a number of DEPENDENTs.

 Each employee may

 For each dependent, the DB keeps a record of name, sex, birthdate, and

relationship to the employee.

Based on the requirements, we can identify four initial entity types in the COMPANY database:

 DEPARTMENT  PROJECT  EMPLOYEE  DEPENDENT Their initial conceptual design is shown on the following slide The initial attributes shown are derived from the requirements description

A

relationship relates two or more distinct entities with a specific

meaning.

 For example, EMPLOYEE John Smith

works on the ProductX

manages the Research

PROJECT, or EMPLOYEE Franklin Wong DEPARTMENT.

Relationships of the same type are grouped or typed into a relationship type.

 For example, the WORKS_ON relationship type in which

EMPLOYEEs and PROJECTs participate, or the MANAGES relationship type in which EMPLOYEEs and DEPARTMENTs participate.

Relationship Type:

 Is the schema description of a relationship  Identifies the relationship name and the participating entity types  Also identifies certain relationship constraints Relationship Set:

 The current set of relationship instances represented in the

database

 The current

state of a relationship type

Degree or arity of a relationship is the number of entity types that participate in it:

• Unary (or recursive) relationship.
• Binary Relationship.
• Ternary relationship. binary relationships.

E.g. Both MANAGES and WORKS_ON are

distinct

A relationship type between the same participating entity type in roles

self-referencing relationship type.

Also called a Example: the SUPERVISION relationship EMPLOYEE participates twice in two distinct roles:

 supervisor (or boss) role  supervisee (or subordinate) role Each relationship instance relates two distinct EMPLOYEE entities:

 One employee in

supervisor role

 One employee in

supervisee role

In a recursive relationship type.

 Both participations are same entity type in different roles.

 For example, SUPERVISION relationships between EMPLOYEE (in role of

supervisor or boss) and (another) EMPLOYEE (in role of subordinate or worker).

In following figure, first role participation labeled with 1 and second role participation labeled with 2.

In ER diagram, need to display role names to distinguish participations.

A relationship type can have attributes:

 For example, HoursPerWeek of WORKS_ON  Its value for each relationship instance describes the number of hours per

week that an EMPLOYEE works on a PROJECT.

 A value of HoursPerWeek depends on a particular (employee, project)

combination

 Most relationship attributes are used with M:N relationships

 In 1:N relationships, they can be transferred to the entity type on the N -

side of the relationship

By examining the requirements, six relationship types are identified All are

binary relationships(degree 2)

Listed below with their participating entity types:

 WORKS_FOR (between EMPLOYEE, DEPARTMENT)  MANAGES (also between EMPLOYEE, DEPARTMENT)  CONTROLS (between DEPARTMENT, PROJECT)  WORKS_ON (between EMPLOYEE, PROJECT)  SUPERVISION (between EMPLOYEE (as subordinate), EMPLOYEE (as supervisor))  DEPENDENTS_OF (between EMPLOYEE, DEPENDENT)

One should choose names that convey, as much as possible, the meanings attached to the different constructs in the schema.

Nouns for entity type and attribute names and verbs for relationship type names.

Choosing binary relationship names to make the ER diagram readable from left to right and from top to bottom.

Entity type and relationship type names in uppercase letters, attribute names capitalized, and role names in lowercase.

We have a bank called SBS. With the following business requirements:

• The bank has multiple branches. Each branch is located in a specific city and has its own unique name. Every year the branch is assigned a yearly budget, and a rating (out of 10).
• A customer is identified by their name and phone number. The bank also keeps track of their address.
• The bank offers two services: accounts and loans to the customers. Each account and loan has a unique number and is created and maintained by a single branch.
• Each account is assigned to one or more customers and the balance cannot be negative.
• A loan is always assigned to a single customer, has interest rate and balance that cannot be negative.

Specified on each participation of an entity type E in a relationship type R Specifies that each entity e in E participates in at least

min and at most relationship instances

max

in R Default(no constraint): min=0, max=n (signifying no limit) Must have min  max, min  0, max  1 Derived from the knowledge of mini-world constraints Examples:

 A department has exactly one manager and an employee can manage at most one department.

 Specify (0,1) for participation of EMPLOYEE in MANAGES  Specify (1,1) for participation of DEPARTMENT in MANAGES

 An employee can work for exactly one department but a department can have any number of

employees.

 Specify (1,1) for participation of EMPLOYEE in WORKS_FOR  Specify (0,n) for participation of DEPARTMENT in WORKS_FOR

Relationship types of degree 2 are called binary Relationship types of degree 3 are called ternary and of degree n are called n-ary In general, an n-ary relationship is not equivalent to n binary relationships Constraints are harder to specify for higher-degree relationships (n > 2) than for binary relationships

In general, 3 binary relationships can represent different information than a single ternary relationship (see Figure 3.17a and b on previous slide) If needed, the binary and n-ary relationships can all be included in the schema design (see Figure 3.17a and b, where all relationships convey different meanings) In some cases, a ternary relationship can be represented as a weak entity if the data model allows a weak entity type to have multiple identifying relationships (and hence multiple owner entity types) (see Figure 3.17c)

If a particular binary relationship can always be derived from a higher-degree relationship, then it is redundant For example, the TAUGHT_DURING, and OFFERED_DURING binary relationships in Figure 3.18 (see next slide) can be derived from the ternary relationship OFFERS (based on the meaning of the relationships).

The (min, max) constraints can be displayed on the edges – however, they do not fully describe the constraints.

A (min, max) on a participation here specifies that each entity is related to at least min and at most max relationship instances in the relationship set..

In general, both (min, max) and 1, M, or N are needed to describe the constraints fully.

Overall, the constraint specification is difficult and possibly ambiguous when we consider relationships of a degree higher than two.

To keep track of the enrollments in classes and student grades, another database is to be designed.

It keeps track of the COLLEGEs, DEPARTMENTs within each college, the COURSEs offered by departments, and SECTIONs of courses, INSTRUCTORs who teach the sections etc.

These entity types and the relationships among these entity types are shown on the next slide.

• ER Model Concepts: Entities, attributes, relationships
• We saw constraints in the ER model (Keys, participation, cardinality)
• The process of conceptual design is subjective, many possible designs
• Common choices include:
  • Entity vs. attribute, entity vs. relationship, binary or n-ary relationship
• The important thing is to continue to refine your design. Your decisions will be more informed when we cover future chapters.

The entity relationship model in its original form did not support the specialization and generalization abstractions Next chapter illustrates how the ER model can be extended with

 Type-subtype and set-subset relationships  Specialization/Generalization Hierarchies  Notation to display them in EER diagrams