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Hi everyone..Assalamualaikum & greetings everyone.  I am Mdm. Jihadah Ahmad your lecturer.This is an additional online platform for Database Fundamentals class activities.  Our main platform will still be the KUPTM LMS. I shall update the content weekly according to our class progress. I do hope you enjoy the class & best wishes

http://e-attendance.kptm.edu.my/attendanceappl/login.php

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Chapter 4 - Entity Relationship Modelling

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Chapter 4 - Entity Relationship Modelling

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chapter-4 Entity Relationship Modelling (ERD)

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Data models define how the logical structure of a database is modeled. Data Models are fundamental entities to introduce abstraction in a DBMS. Data models define how data is connected to each other and how they are processed and stored inside the system.

The very first data model could be flat data-models, where all the data used are to be kept in the same plane. Earlier data models were not so scientific, hence they were prone to introduce lots of duplication and update anomalies.

Entity-Relationship Model

Entity-Relationship (ER) Model is based on the notion of real-world entities and relationships among them. While formulating real-world scenario into the database model, the ER Model creates entity set, relationship set, general attributes and constraints.

ER Model is best used for the conceptual design of a database.

ER Model is based on −

These concepts are explained below.

Entity − An entity in an ER Model is a real-world entity having properties called attributes. Every attribute is defined by its set of values called domain. For example, in a school database, a student is considered as an entity. Student has various attributes like name, age, class, etc.

Relationship − The logical association among entities is called relationship. Relationships are mapped with entities in various ways. Mapping cardinalities define the number of association between two entities.

Mapping cardinalities −

Relational Model

The most popular data model in DBMS is the Relational Model. It is more scientific a model than others. This model is based on first-order predicate logic and defines a table as an n-ary relation.

The main highlights of this model are −

Data is stored in tables called relations.

Relations can be normalized.

In normalized relations, values saved are atomic values.

Each row in a relation contains a unique value.

Each column in a relation contains values from a same domain.

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DBMS Data Models - Data models define how the logical structure of a database is modeled. Data Models are fundamental entities to introduce abstraction in a DBMS. Data models define how data is connected to each other and how they are processed and stored inside the system.

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8 minute read

Types of database models

There are many kinds of data models. Some of the most common ones include:

Hierarchical database model

Relational model

Network model

Object-oriented database model

Entity-relationship model

Document model

Entity-attribute-value model

Star schema

The object-relational model, which combines the two that make up its name

You may choose to describe a database with any one of these depending on several factors. The biggest factor is whether the database management system you are using supports a particular model. Most database management systems are built with a particular data model in mind and require their users to adopt that model, although some do support multiple models.

In addition, different models apply to different stages of the database design process. High-level conceptual data models are best for mapping out relationships between data in ways that people perceive that data. Record-based logical models, on the other hand, more closely reflect ways that the data is stored on the server.

Selecting a data model is also a matter of aligning your priorities for the database with the strengths of a particular model, whether those priorities include speed, cost reduction, usability, or something else.

Let's take a closer look at some of the most common database models.

Relational model

The most common model, the relational model sorts data into tables, also known as relations, each of which consists of columns and rows. Each column lists an attribute of the entity in question, such as price, zip code, or birth date. Together, the attributes in a relation are called a domain. A particular attribute or combination of attributes is chosen as a primary key that can be referred to in other tables, when it's called a foreign key.

Each row, also called a tuple, includes data about a specific instance of the entity in question, such as a particular employee.

The model also accounts for the types of relationships between those tables, including one-to-one, one-to-many, and many-to-many relationships. Here's an example:

Within the database, tables can be normalized, or brought to comply with normalization rules that make the database flexible, adaptable, and scalable. When normalized, each piece of data is atomic, or broken into the smallest useful pieces.

Relational databases are typically written in Structured Query Language (SQL). The model was introduced by E.F. Codd in 1970.

Hierarchical model

The hierarchical model organizes data into a tree-like structure, where each record has a single parent or root. Sibling records are sorted in a particular order. That order is used as the physical order for storing the database. This model is good for describing many real-world relationships.

This model was primarily used by IBM's Information Management Systems in the 60s and 70s, but they are rarely seen today due to certain operational inefficiencies.

Network model

The network model builds on the hierarchical model by allowing many-to-many relationships between linked records, implying multiple parent records. Based on mathematical set theory, the model is constructed with sets of related records. Each set consists of one owner or parent record and one or more member or child records. A record can be a member or child in multiple sets, allowing this model to convey complex relationships.

It was most popular in the 70s after it was formally defined by the Conference on Data Systems Languages (CODASYL).

Object-oriented database model

This model defines a database as a collection of objects, or reusable software elements, with associated features and methods. There are several kinds of object-oriented databases:

A multimedia database incorporates media, such as images, that could not be stored in a relational database.

A hypertext database allows any object to link to any other object. It's useful for organizing lots of disparate data, but it's not ideal for numerical analysis.

The object-oriented database model is the best known post-relational database model, since it incorporates tables, but isn't limited to tables. Such models are also known as hybrid database models.

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Object-relational model

This hybrid database model combines the simplicity of the relational model with some of the advanced functionality of the object-oriented database model. In essence, it allows designers to incorporate objects into the familiar table structure.

Languages and call interfaces include SQL3, vendor languages, ODBC, JDBC, and proprietary call interfaces that are extensions of the languages and interfaces used by the relational model.

Entity-relationship model

This model captures the relationships between real-world entities much like the network model, but it isn't as directly tied to the physical structure of the database. Instead, it's often used for designing a database conceptually.

Here, the people, places, and things about which data points are stored are referred to as entities, each of which has certain attributes that together make up their domain. The cardinality, or relationships between entities, are mapped as well.

A common form of the ER diagram is the star schema, in which a central fact table connects to multiple dimensional tables.

Other database models

A variety of other database models have been or are still used today.

Inverted file model

A database built with the inverted file structure is designed to facilitate fast full text searches. In this model, data content is indexed as a series of keys in a lookup table, with the values pointing to the location of the associated files. This structure can provide nearly instantaneous reporting in big data and analytics, for instance.

This model has been used by the ADABAS database management system of Software AG since 1970, and it is still supported today.

Flat model

The flat model is the earliest, simplest data model. It simply lists all the data in a single table, consisting of columns and rows. In order to access or manipulate the data, the computer has to read the entire flat file into memory, which makes this model inefficient for all but the smallest data sets.

Multidimensional model

This is a variation of the relational model designed to facilitate improved analytical processing. While the relational model is optimized for online transaction processing (OLTP), this model is designed for online analytical processing (OLAP).

Each cell in a dimensional database contains data about the dimensions tracked by the database. Visually, it's like a collection of cubes, rather than two-dimensional tables.

Semistructured model

In this model, the structural data usually contained in the database schema is embedded with the data itself. Here the distinction between data and schema is vague at best. This model is useful for describing systems, such as certain Web-based data sources, which we treat as databases but cannot constrain with a schema. It's also useful for describing interactions between databases that don't adhere to the same schema.

Context model

This model can incorporate elements from other database models as needed. It cobbles together elements from object-oriented, semistructured, and network models.

Associative model

This model divides all the data points based on whether they describe an entity or an association. In this model, an entity is anything that exists independently, whereas an association is something that only exists in relation to something else.

The associative model structures the data into two sets:

A set of items, each with a unique identifier, a name, and a type

A set of links, each with a unique identifier and the unique identifiers of a source, verb, and target. The stored fact has to do with the source, and each of the three identifiers may refer either to a link or an item.

Other, less common database models include:

Semantic model, which includes information about how the stored data relates to the real world

XML database, which allows data to be specified and even stored in XML format

Named graph

Triplestore

NoSQL database models

In addition to the object database model, other non-SQL models have emerged in contrast to the relational model:

The graph database model, which is even more flexible than a network model, allowing any node to connect with any other.

The multivalue model, which breaks from the relational model by allowing attributes to contain a list of data rather than a single data point.

The document model, which is designed for storing and managing documents or semi-structured data, rather than atomic data.

Databases on the Web

Most websites rely on some kind of database to organize and present data to users. Whenever someone uses the search functions on these sites, their search terms are converted into queries for a database server to process. Typically, middleware connects the web server with the database.

The broad presence of databases allows them to be used in almost any field, from online shopping to micro-targeting a voter segment as part of a political campaign. Various industries have developed their own norms for database design, from air transport to vehicle manufacturing.

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In-depth explanation and comparison of all the major database models, with examples, definitions, and more.

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What is Data Model in DBMS and what are its types?

Data Model

Data Model gives us an idea that how the final system will look like after its complete implementation. It defines the data elements and the relationships between the data elements. Data Models are used to show how data is stored, connected, accessed and updated in the database management system. Here, we use a set of symbols and text to represent the information so that members of the organisation can communicate and understand it. Though there are many data models being used nowadays but the Relational model is the most widely used model. Apart from the Relational model, there are many other types of data models about which we will study in details in this blog. Some of the Data Models in DBMS are:

1. Hierarchical Model

2. Network Model

3. Entity-Relationship Model

4. Relational Model

5. Object-Oriented Data Model

6. Object-Relational Data Model

7. Flat Data Model

8. Semi-Structured Data Model

9. Associative Data Model

10. Context Data Model

Hierarchical Model

Hierarchical Model was the first DBMS model. This model organises the data in the hierarchical tree structure. The hierarchy starts from the root which has root data and then it expands in the form of a tree adding child node to the parent node. This model easily represents some of the real-world relationships like food recipes, sitemap of a website etc. Example: We can represent the relationship between the shoes present on a shopping website in the following way:

Features of a Hierarchical Model

1. One-to-many relationship: The data here is organised in a tree-like structure where the one-to-many relationship is between the datatypes. Also, there can be only one path from parent to any node. Example: In the above example, if we want to go to the node sneakers we only have one path to reach there i.e through men's shoes node.

2. Parent-Child Relationship: Each child node has a parent node but a parent node can have more than one child node. Multiple parents are not allowed.

3. Deletion Problem: If a parent node is deleted then the child node is automatically deleted.

4. Pointers: Pointers are used to link the parent node with the child node and are used to navigate between the stored data. Example: In the above example the ' shoes ' node points to the two other nodes ' women shoes ' node and ' men's shoes ' node.

Advantages of Hierarchical Model

It is very simple and fast to traverse through a tree-like structure.

Any change in the parent node is automatically reflected in the child node so, the integrity of data is maintained.

Disadvantages of Hierarchical Model

Complex relationships are not supported.

As it does not support more than one parent of the child node so if we have some complex relationship where a child node needs to have two parent node then that can't be represented using this model.

If a parent node is deleted then the child node is automatically deleted.

Network Model

This model is an extension of the hierarchical model. It was the most popular model before the relational model. This model is the same as the hierarchical model, the only difference is that a record can have more than one parent. It replaces the hierarchical tree with a graph. Example: In the example below we can see that node student has two parents i.e. CSE Department and Library. This was earlier not possible in the hierarchical model.

Features of a Network Model

1. Ability to Merge more Relationships: In this model, as there are more relationships so data is more related. This model has the ability to manage one-to-one relationships as well as many-to-many relationships.

2. Many paths: As there are more relationships so there can be more than one path to the same record. This makes data access fast and simple.

3. Circular Linked List: The operations on the network model are done with the help of the circular linked list. The current position is maintained with the help of a program and this position navigates through the records according to the relationship.

Advantages of Network Model

The data can be accessed faster as compared to the hierarchical model. This is because the data is more related in the network model and there can be more than one path to reach a particular node. So the data can be accessed in many ways.

As there is a parent-child relationship so data integrity is present. Any change in parent record is reflected in the child record.

Disadvantages of Network Model

As more and more relationships need to be handled the system might get complex. So, a user must be having detailed knowledge of the model to work with the model.

Any change like updation, deletion, insertion is very complex.

Entity-Relationship Model

Entity-Relationship Model or simply ER Model is a high-level data model diagram. In this model, we represent the real-world problem in the pictorial form to make it easy for the stakeholders to understand. It is also very easy for the developers to understand the system by just looking at the ER diagram. We use the ER diagram as a visual tool to represent an ER Model. ER diagram has the following three components:

Entities: Entity is a real-world thing. It can be a person, place, or even a concept. Example: Teachers, Students, Course, Building, Department, etc are some of the entities of a School Management System.

Attributes: An entity contains a real-world property called attribute. This is the characteristics of that attribute. Example: The entity teacher has the property like teacher id, salary, age, etc.

Relationship: Relationship tells how two attributes are related. Example: Teacher works for a department.

Example:

In the above diagram, the entities are Teacher and Department. The attributes of Teacher entity are Teacher_Name, Teacher_id, Age, Salary, Mobile_Number. The attributes of entity Department entity are Dept_id, Dept_name. The two entities are connected using the relationship. Here, each teacher works for a department.

Features of ER Model

Graphical Representation for Better Understanding: It is very easy and simple to understand so it can be used by the developers to communicate with the stakeholders.

ER Diagram: ER diagram is used as a visual tool for representing the model.

Database Design: This model helps the database designers to build the database and is widely used in database design.

Advantages of ER Model

Simple: Conceptually ER Model is very easy to build. If we know the relationship between the attributes and the entities we can easily build the ER Diagram for the model.

Effective Communication Tool : This model is used widely by the database designers for communicating their ideas.

Easy Conversion to any Model : This model maps well to the relational model and can be easily converted relational model by converting the ER model to the table. This model can also be converted to any other model like network model, hierarchical model etc.

Disadvatages of ER Model

No industry standard for notation: There is no industry standard for developing an ER model. So one developer might use notations which are not understood by other developers.

Hidden information: Some information might be lost or hidden in the ER model. As it is a high-level view so there are chances that some details of information might be hidden.

Relational Model

Relational Model is the most widely used model. In this model, the data is maintained in the form of a two-dimensional table. All the information is stored in the form of row and columns. The basic structure of a relational model is tables. So, the tables are also called relations in the relational model. Example: In this example, we have an Employee table.

Features of Relational Model

Tuples : Each row in the table is called tuple. A row contains all the information about any instance of the object. In the above example, each row has all the information about any specific individual like the first row has information about John.

Attribute or field: Attributes are the property which defines the table or relation. The values of the attribute should be from the same domain. In the above example, we have different attributes of the employee like Salary, Mobile_no, etc.

Advnatages of Relational Model

Simple: This model is more simple as compared to the network and hierarchical model.

Scalable: This model can be easily scaled as we can add as many rows and columns we want.

Structural Independence: We can make changes in database structure without changing the way to access the data. When we can make changes to the database structure without affecting the capability to DBMS to access the data we can say that structural independence has been achieved.

Disadvantages of Relatinal Model

Hardware Overheads: For hiding the complexities and making things easier for the user this model requires more powerful hardware computers and data storage devices.

Bad Design: As the relational model is very easy to design and use. So the users don't need to know how the data is stored in order to access it. This ease of design can lead to the development of a poor database which would slow down if the database grows.

But all these disadvantages are minor as compared to the advantages of the relational model. These problems can be avoided with the help of proper implementation and organisation.

Object-Oriented Data Model

The real-world problems are more closely represented through the object-oriented data model. In this model, both the data and relationship are present in a single structure known as an object. We can store audio, video, images, etc in the database which was not possible in the relational model(although you can store audio and video in relational database, it is adviced not to store in the relational database). In this model, two are more objects are connected through links. We use this link to relate one object to other objects. This can be understood by the example given below.

In the above example, we have two objects Employee and Department. All the data and relationships of each object are contained as a single unit. The attributes like Name, Job_title of the employee and the methods which will be performed by that object are stored as a single object. The two objects are connected through a common attribute i.e the Department_id and the communication between these two will be done with the help of this common id.

Object-Relational Model

As the name suggests it is a combination of both the relational model and the object-oriented model. This model was built to fill the gap between object-oriented model and the relational model. We can have many advanced features like we can make complex data types according to our requirements using the existing data types. The problem with this model is that this can get complex and difficult to handle. So, proper understanding of this model is required.

Flat Data Model

It is a simple model in which the database is represented as a table consisting of rows and columns. To access any data, the computer has to read the entire table. This makes the modes slow and inefficient.

Semi-Structured Model

Semi-structured model is an evolved form of the relational model. We cannot differentiate between data and schema in this model. Example: Web-Based data sources which we can't differentiate between the schema and data of the website. In this model, some entities may have missing attributes while others may have an extra attribute. This model gives flexibility in storing the data. It also gives flexibility to the attributes. Example: If we are storing any value in any attribute then that value can be either atomic value or a collection of values.

Associative Data Model

Associative Data Model is a model in which the data is divided into two parts. Everything which has independent existence is called as an entity and the relationship among these entities are called association . The data divided into two parts are called items and links.

Item : Items contain the name and the identifier(some numeric value).

Links: Links contain the identifier, source, verb and subject.

Example : Let us say we have a statement "The world cup is being hosted by London from 30 May 2020". In this data two links need to be stored:

1. The world cup is being hosted by London. The source here is 'the world cup', the verb 'is being' and the target is 'London'.

2. ...from 30 May 2020. The source here is the previous link, the verb is 'from' and the target is '30 May 2020'.

This is represented using the table as follows:

Context Data Model

Context Data Model is a collection of several models. This consists of models like network model, relational models etc. Using this model we can do various types of tasks which are not possible using any model alone.

This is all about the various data model of DBMS. Hope you learned something new today.

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What is Data Model in DBMS and what are its types?

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In this blog, we will learn about various data models present in DBMS. We will also learn about various types of data models present along with advantages and disadvantages of each model.

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What is Data Modelling?

Data modeling (data modelling) is the process of creating a data model for the data to be stored in a database. This data model is a conceptual representation of Data objects, the associations between different data objects, and the rules.

Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.

Data Models in DBMS

The Data Model is defined as an abstract model that organizes data description, data semantics, and consistency constraints of data. The data model emphasizes on what data is needed and how it should be organized instead of what operations will be performed on data. Data Model is like an architect's building plan, which helps to build conceptual models and set a relationship between data items.

The two types of Data Modeling Techniques are

1. Entity Relationship (E-R) Model

2. UML (Unified Modelling Language)

We will discuss them in detail later.

This Data Modeling Tutorial is best suited for freshers, beginners as well as experienced professionals. In this data model tutorial, data modeling concepts in detail-

Why use Data Model?

The primary goal of using data model are:

Ensures that all data objects required by the database are accurately represented. Omission of data will lead to creation of faulty reports and produce incorrect results.

A data model helps design the database at the conceptual, physical and logical levels.

Data Model structure helps to define the relational tables, primary and foreign keys and stored procedures.

It provides a clear picture of the base data and can be used by database developers to create a physical database.

It is also helpful to identify missing and redundant data.

Though the initial creation of data model is labor and time consuming, in the long run, it makes your IT infrastructure upgrade and maintenance cheaper and faster.

Types of Data Models in DBMS

Types of Data Models: There are mainly three different types of data models: conceptual data models, logical data models, and physical data models, and each one has a specific purpose. The data models are used to represent the data and how it is stored in the database and to set the relationship between data items.

1. Conceptual Data Model: This Data Model defines WHAT the system contains. This model is typically created by Business stakeholders and Data Architects. The purpose is to organize, scope and define business concepts and rules.

2. Logical Data Model: Defines HOW the system should be implemented regardless of the DBMS. This model is typically created by Data Architects and Business Analysts. The purpose is to developed technical map of rules and data structures.

3. Physical Data Model: This Data Model describes HOW the system will be implemented using a specific DBMS system. This model is typically created by DBA and developers. The purpose is actual implementation of the database.

Types of Data Model

Conceptual Data Model

A Conceptual Data Model is an organized view of database concepts and their relationships. The purpose of creating a conceptual data model is to establish entities, their attributes, and relationships. In this data modeling level, there is hardly any detail available on the actual database structure. Business stakeholders and data architects typically create a conceptual data model.

The 3 basic tenants of Conceptual Data Model are

Entity: A real-world thing

Attribute: Characteristics or properties of an entity

Relationship: Dependency or association between two entities

Data model example:

Customer and Product are two entities. Customer number and name are attributes of the Customer entity

Product name and price are attributes of product entity

Sale is the relationship between the customer and product

Conceptual Data Model

Characteristics of a conceptual data model

Offers Organisation-wide coverage of the business concepts.

This type of Data Models are designed and developed for a business audience.

The conceptual model is developed independently of hardware specifications like data storage capacity, location or software specifications like DBMS vendor and technology. The focus is to represent data as a user will see it in the "real world."

Conceptual data models known as Domain models create a common vocabulary for all stakeholders by establishing basic concepts and scope.

Logical Data Model

The Logical Data Model is used to define the structure of data elements and to set relationships between them. The logical data model adds further information to the conceptual data model elements. The advantage of using a Logical data model is to provide a foundation to form the base for the Physical model. However, the modeling structure remains generic.

Logical Data Model

At this Data Modeling level, no primary or secondary key is defined. At this Data modeling level, you need to verify and adjust the connector details that were set earlier for relationships.
Characteristics of a Logical data model

Describes data needs for a single project but could integrate with other logical data models based on the scope of the project.

Designed and developed independently from the DBMS.

Data attributes will have datatypes with exact precisions and length.

Normalization processes to the model is applied typically till 3NF.

Physical Data Model

A Physical Data Model describes a database-specific implementation of the data model. It offers database abstraction and helps generate the schema. This is because of the richness of meta-data offered by a Physical Data Model. The physical data model also helps in visualizing database structure by replicating database column keys, constraints, indexes, triggers, and other RDBMS features.

Physical Data Model

Characteristics of a physical data model:

The physical data model describes data need for a single project or application though it maybe integrated with other physical data models based on project scope.

Data Model contains relationships between tables that which addresses cardinality and nullability of the relationships.

Developed for a specific version of a DBMS, location, data storage or technology to be used in the project.

Columns should have exact datatypes, lengths assigned and default values.

Primary and Foreign keys, views, indexes, access profiles, and authorizations, etc. are defined.

Advantages and Disadvantages of Data Model:
Advantages of Data model:

The main goal of a designing data model is to make certain that data objects offered by the functional team are represented accurately.

The data model should be detailed enough to be used for building the physical database.

The information in the data model can be used for defining the relationship between tables, primary and foreign keys, and stored procedures.

Data Model helps business to communicate the within and across organizations.

Data model helps to documents data mappings in ETL process

Help to recognize correct sources of data to populate the model

Disadvantages of Data model:

To develop Data model one should know physical data stored characteristics.

This is a navigational system produces complex application development, management. Thus, it requires a knowledge of the biographical truth.

Even smaller change made in structure require modification in the entire application.

There is no set data manipulation language in DBMS.

Conclusion

Data modeling is the process of developing data model for the data to be stored in a Database.

Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.

Data Model structure helps to define the relational tables, primary and foreign keys and stored procedures.

There are three types of conceptual, logical, and physical.

The main aim of conceptual model is to establish the entities, their attributes, and their relationships.

Logical data model defines the structure of the data elements and set the relationships between them.

A Physical Data Model describes the database specific implementation of the data model.

The main goal of a designing data model is to make certain that data objects offered by the functional team are represented accurately.

The biggest drawback is that even smaller change made in structure require modification in the entire application.

Reading this Data Modeling tutorial, you will learn from the basic concepts such as What is Data Model? Introduction to different types of Data Model, advantages, disadvantages, and data model example.

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What is Data Modelling? Types (Conceptual, Logical, Physical)

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Data modeling (data modelling) is the process of creating a data model for the data to be stored in a database.

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HOW TO DOWNLOAD & INSTALL MYSQL (updated)

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Sampel Database Proposal Project Report (Part A)- Gucci Store

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MySQL Tutorial - MySQL By Examples for Beginners

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DROP DATABASE databaseName DROP DATABASE IF EXISTS databaseName CREATE DATABASE databaseName CREATE DATABASE IF NOT EXISTS databaseName SHOW DATABASES USE databaseName SELECT DATABASE() SHOW CREATE DATABASE databaseName DROP TABLE [IF EXISTS] tableName, ... CREATE TABLE [IF NOT EXISTS] tableName ( columnName columnType columnAttribute, ...

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MySQL is a widely used relational database management system (RDBMS). MySQL is free and open-source. MySQL is ideal for both small and large applications. Start learning MySQL now " With our online MySQL editor, you can edit the SQL statements, and click on a button to view the result.