CS代考 COMM1822 as the course ID. – cscodehelp代写

Introduction to Databases for Business Analytics
Week 1 Entity Relationship (ER) Modelling Part 1
Term 2 2022
Lecturer: Kam-Fung (Henry) -mail:

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Acknowledgement of Country
UNSW Business School acknowledges the Bidjigal (Kensington campus) and Gadigal (City campus) the traditional custodians of the lands where each campus is located.
We acknowledge all Aboriginal and Islander Elders, past and present and their communities who have shared and practiced their teachings over thousands of years including business practices.
We recognise Aboriginal and Islander people’s ongoing leadership and contributions, including to business, education and industry.
UNSW Business School. (2022, May 7). Acknowledgement of Country [online video]. Retrieved from https://vimeo.com/369229957/d995d8087f

• Data Modelling
❑ Data model as a (relatively) simple abstraction of the complex real-world (for the purpose of creating a DB).
❑ A good DBMS will perform poorly with a poorly designed database.
❑ One modelling technique to design a database: Entity Relationship Modelling
• Entity Relationship Modelling ❑ Entity Types and Entity Instances ❑ Attributes and Values
❑ Relationships ❑ Connectivity ❑ Cardinality

Chapter 2 Data Models 2-1 to 2-6
Entity Relationship (ER) Modelling
4-1 to 4-2

Data Modeling and Data Models
• Model – Abstraction of a real-world object or event
• Data modeling: Iterative and progressive process of creating a specific data model for a determined problem domain
To model and translate the business requirements into a data model they can be used to store data which to business can use
• Datamodels:Simplerepresentationsofcomplexreal-worlddata structures
• Useful for supporting a specific problem domain
What would be the data model like for UNSW?

Importance of Data Models
Are a communication tool
Give an overall view of the database
Organize data for various users
Are an abstraction for the creation of good

Data Model Basic Building Blocks
Entity: Unique and distinct object used to collect and store
❑ Attribute: Characteristic of an entity
Relationship: Describes an association among entities
❑One-to-many (1:M) ❑Many-to-many (M:N or M:M) ❑One-to-one (1:1)
Consider the following entities: professors, students, courses, departments, research centres, etc.
e.g., people, thing, event, …
Constraint: Set of rules to ensure data integrity Violation examples:
• Enter an SID to STUDENT table, it finds two students (Entity integrity)
• STUDENT table says that student X’s department code is Y. But in the DEPARTMENT Table, there is no code Y. (Referential integrity)

Business Rules – Design a Data Model
Brief, precise, and unambiguous description of a policy, procedure, or
Enable defining the basic building
Describe main and distinguishing
characteristics of the data

Translating Business Rules into Data Model Components
• Nouns translate into entities
• Verbs translate into relationships among entities
• Relationships are bidirectional
Each student can take at most 3 courses each semester. Each course may have up to 300 students at UNSW.
• Questions to identify the relationship type: 1:1, 1:M or M:N ❑How many instances of B are related to one instance of A?
❑How many instances of A are related to one instance of B?
Each student can take at most 3 courses each semester. Each research student must have two supervisors at UNSW.

Naming Conventions
Entity names – Required to
❑ Be descriptive of the objects in the business environment ❑ Use terminology that is familiar to the users
Attribute name – Required to be descriptive of the data represented by
the attribute
STUDENT: SID, DOB, GENDER, FIRST_NAME, LAST_NAME, …
Proper naming
Facilitates communication between parties Promotes self-documentation
STUDENT, EMPLOYEE, DEPARTMENT, …

Evolution of Data Models
We will focus and discuss more on relational, entity relationship, and NoSQL in the last few weeks.
Source: Coronel, Morris, Rob 2017

Levels of Data Abstraction
Degree of Abstraction
Independent of
End user views
Hardware and software
Conceptual
Global view of data (database model independent)
Hardware and software
Specific database model
Storage and access methods
Neither Hardware nor software
Data abstraction is the reduction of a particular body of data to a simplified representation of the whole.
Abstraction, in general, is the process of taking away or removing characteristics from something in order to reduce it to a set of essential characteristic.
Source: Coronel, Morris, Rob 2017

Conceptual Data Modelling Techniques Two common techniques:
• Entity-Relationship (ER) modelling: Top-down approach. Begins by looking for the data groups in the system.
• Normalization: Bottom-up approach. Begins by looking at the smallest individual items of data recorded by the system.

Internal Model and Conceptual Model
• The internal model is the model that we used when database is implemented.
• The internal model maps the conceptual model to the DBMS.
• The internal model depends on the specific database software.
• Hence, a change in DBMS software requires internal model be changed.
• Logical independence: you can change the internal model without affecting conceptual model!

Conceptual Model PName
Department
Conceptual Model
PROFESSOR (PName, DOB, Address, Department) COURSE (CourseID, CName, PName)
Internal Model

Conceptual Modelling: ER Model
• An Entity-Relationship (ER) model is a detailed, logical representation of the data for an organisation or for a business area.
• The ER model is expressed in terms of entities in the business environment, the relationships or associations among those entities, and the attributes of both the entities and their relationships.
• An ER model is normally expressed as an ER diagram, which is a graphical representation of an ER model. In this course we will follow Chen’s notation.

ER Model Notations

• Data Modelling
❑ Data model as a (relatively) simple abstraction of the complex real-world (for the purpose of
creating a DB).
❑ A good DBMS will perform poorly with a poorly designed database.
❑ One modelling technique to design a database: Entity Relationship Modelling
• Entity Relationship Modelling ❑ Entity Types and Entity Instances ❑ Attributes and Values
❑ Relationships ❑ Connectivity ❑ Cardinality

Entity Relationship Modelling (ERM)
Basis of an entity relationship diagram (ERD)
ERD depicts the:
❑ Conceptual database as viewed by end user
❑ Database’s main components
❑ Entities
❑ Attributes
❑ Relationships (Associations between tables)
Entity – Refers to the entity set and not to a single entity occurrence
(Columns of tables)

Entity (Type) and (Entity) Instance
• en·ti·ty /ˈentitē/ (Noun)
❑ A thing with distinct and independent existence. ❑ Existence; being: “entity and nonentity”.
• Synonyms: being – existence – essence – thing
• in·stance /ˈinstəns/ (Noun)
❑ An example or single occurrence of something: “an instance of corruption”. ❑ A particular case: “in this instance”.
• Synonyms: example – case – sample – event – occurrence – exemplar
• One type of things is a Person. Joe is an instance of Person.
• One type of things is a Drink. Espresso an instance of Drink.

Entity (Type) and (Entity) Instance
• Entities: “An entity is an object about which the system requires to hold data.”
• An entity type (entity class) is a collection of entities that share common properties or characteristics (similar to be grouped into one Entity Type).
• It is represented as a rectangle box in the ER model diagram with the name of the entity inside.
• An entity instance is a single occurrence of an entity type. STUDENT
Entity Type
STUDENT z1234567 z1357926
Entity Instances
Martin, S. BEng Fong, L. BSc

ER Model with Only Appropriate Entities
A treasurer looks after researchers’ research accounts. Each account pays more than one expenses. The treasure prints expense reports regularly, e.g., every month.
ACCOUNT 1 has M EXPENSE

Characteristics of entities
• Required attribute: Must have a value, cannot be left empty
• Optional attribute: Does not require a value, can be left empty
• Domain: Set of possible values for a given attribute
• Identifiers: One or more attributes that uniquely identify each entity instance
called Keys in the relational model

Attributes
Simple attribute: Attribute that cannot be subdivided ❑ Example: zID
Composite attribute: Attribute that can be subdivided to yield additional
attributes
❑ Example: Address (= street, city, state, area code)
Single-valued attribute: Attribute that has only a single value Multivalued attribute: Attribute that have many values Derived attribute: Attribute that derived using an algorithm
A Key attribute is unique so to identify the entity.

Example of Attributes
Department
Entity: PROFESSOR
Attribute: PName, Department, Address, DOB
Composite attribute: Address

A Multivalued Attribute in an Entity
multiple values

Splitting the Multivalued Attributes into
Is this a good idea? Depends on your design!

Depiction of a Derived Attribute
Derived attribute is when the value is calculated from other attributes. e.g., EMP_AGE can be calculated from EMP_DOB.
Do we need EMP_AGE?
Technically, we do not store the employee’s age because we can calculate from date of birth of the employee. Otherwise, you have to update the age every day.

Advantages and Disadvantages of Storing Derived Attributes
Derived Attribute: Stored
Derived Attribute: Not Stored
• Saves CPU processing cycles
• Saves data access time
• Data value is readily available
• Can be used to keep track of historical data
• Saves storage space
• Computation always yields current value
Disadvantage
• Requires constant maintenance to ensure derived value is current, especially if any values used in the calculation change
• Uses CPU processing cycles
• Increases data access time
• Adds coding complexity to queries

Consist of one or more attributes that determine other attributes
❑ Ensure that each row in a table is uniquely identifiable
❑ Establish relationships among tables and to ensure the integrity of the data
Primary key (PK): Attribute or combination of attributes that uniquely identifies any given row
For example, STUDENTS table, the PK is zID; EMPLOYEES table, PK is employee ID. A PK may contain more than one attribute.

Find the Primary Keys
Table: CLASS_ENR
Table: STUDENT
28/02/2010
Table: COURSE
CourseName
Intro to DB for BusAn
Table: DEGREE
For simplicity, not all attributes are included in the tables. Also, I used COMM1822 as the course ID.

Answer for the Primary Keys
Table: STUDENT
unsw.edu.au
28/02/2010
Table: CLASS_ENR
Table: DEGREE
Table: COURSE
CourseName
Intro to DB for BusAn

Types of Keys
• Composite key: Key that is composed of more than one attribute
e.g., the CLASS_ENR table has (zID, CourseID, TermID) is a composite key
• Key attribute: Attribute that is a part of a key
• Superkey: Key that can uniquely identify any row in the table
e.g., zID, {zID, Last_Name}, {zID, First_Name}, {zID, Frist_Name, Last_Name}, … in STUDENT table
• Candidate key: Minimal superkey
e.g., zID in STUDENT table; mobile number can identify you if you forget your rewards card.
• Entity integrity: Condition in which each row in the table has its own unique identity ❑ All of the values in the primary key must be unique
❑ No key attribute in the primary key can contain a null

Types of Keys
• Null: Absence of any data value that could represent ❑ An unknown attribute value
❑ A known, but missing, attribute value
❑ An inapplicable condition
• Referential integrity: Every reference to an entity instance by another entity instance is valid
• Foreign key (FK): Primary key of one table that has been placed into another table to create a common attribute
• Secondary key: Key used strictly for data retrieval purposes
e.g., people do not remember their membership no. (PK), the secondary key can be their name, which may not be unique.

Example of Foreign Key
Table: CLASS_ENR
Table: STUDENT
unsw.edu.au
28/02/2010
Table: COURSE
CourseName
Intro to DB for BusAn
Table: DEGREE

Relationships
A relationship is a link between two entities which is significant for the system.
• The degree of a relationship is the number of entity types that participate in that relationship.
• The most common relationships are unary, binary, ternary, and quaternary.
• The relationships between entities can be ❑ One-to-One 1:1
❑ One-to-Many 1:M ❑ Many-to-Many M:N

Entity Relationships teaches
Entity 1 Relationship Entity 2
Binary Relationship (2 entities)
is married to
Unary Relationship (1 entity)

Ternary Relationship (3 entities)
Quaternary Relationship (4 entities)
FINACIAL_ INSTITUTE

Connectivity
• Connectivity is used to describe the relationship classification.
• The ER diagram indicates connectivity by using a numeric notation.

Basic Relationship (One-to-One 1:1)
CAPITAL_CITY
NSW Victoria
Capital City

Basic Relationship (One-to-Many 1:M)
1 stocked M _as
• A movie (e.g., Avengers) can be stocked as several blurays (e.g., 30 copies)
• All blurays contain a film.
• There is “one-to-many” relationship between film and bluray.

Basic Relationship (Many-to-Many M:N)
Other_Attributes
CourseName
Prerequisite
• A student enrolls many courses.
• Each course is enrolled by many students.

Cardinality
• Cardinality expresses the specific number of entity occurrences associated with
one occurrence of related entity.
• A cardinality constraint specifies the number of instances of entity A that can be associated with
each instance of entity B. Cardinality constraints are derived from business rules.
• Business rules: They are derived from organisation’s data environment.
• Minimum cardinality is the minimum number of instances of one entity that may be associated with each instance of another entity.
• Maximum cardinality is the maximum number of instances of one entity that may be associated with each instance of another entity.

Examples of Cardinality
(0, 3) (1, 1)
Cardinality
One-to-Many Relationship
Cardinality
Many-to-Many Relationship
How to read this?
• A professor teaches (0, 3) classes. A class is taught by (1, 1) professors.
• A student enrolls in (1, 6) classes. A class has enrolled in it (0, 30) students.

Relationship Participation
Optional relationships for MOTHER, mandatory relationship for SON
Mandatory relationship between DOCTOR & PATIENT
A participating entity in a relationship can be either optional or mandatory. Determined by the specific meaning of the terms used.
❑ Dependsoncontext.
❑ Needtostateassumptions.

Ternary Relationships
People or institutions in CONTRIBUTOR group donate money to a special research FUND.
Researchers found in RECIPIENT are funded through the FUND contents.
Research fund may be placed into several categories.
CONTRIBUTOR

Relationship, Connectivity, Cardinality
Relationship: Association between entities that always operate in both
Participants: Entities that participate in a relationship
The most common relationships are unary, binary, ternary, and quaternary.
Connectivity: Describes the relationship classification 1:1, 1:M and M:N
Cardinality: Expresses the minimum and maximum number of entity
occurrences associated with one occurrence of related entity
e.g., how many classes at most one professor can teach.
directions

Recap: ER Modelling Part 1
• Data Modelling
❑ Data model as a (relatively) simple abstraction of the complex real-world (for the purpose of creating a DB).
❑ A good DBMS will perform poorly with a poorly designed database.
❑ One modelling technique to design a database: Entity Relationship Modelling
• Entity Relationship Modelling ❑ Entity Types and Entity Instances ❑ Attributes and Values
❑ Relationships ❑ Connectivity ❑ Cardinality

Source: tryinteract.com

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