The document discusses data modeling and the entity-relationship model. It defines key concepts like entities, attributes, relationships, and cardinalities. Entities have attributes and can be connected through relationships. Relationships can be one-to-one, one-to-many, many-to-one, or many-to-many depending on how many entities can be associated with each other. The entity-relationship model is useful for conceptual database design and represents these concepts visually in diagrams.
The document discusses how to model a database using an entity-relationship (ER) model. It describes the key components of an ER model including entities, attributes, relationships, and keys. It explains how entities can have attributes and how relationships associate entities. It also covers mapping cardinalities, weak entities, specialization/generalization, and how to map an ER diagram to relational database tables.
Entity Relationship Diagram – ER Diagram in DBMS.pptxsukrithlal008
An Entity-Relationship (ER) diagram is a design or blueprint of a database that describes the structure of a database using a diagram. The main components of an ER diagram are entities, attributes, and relationships. An ER diagram shows the relationships among entity sets where an entity set is a group of similar entities that can have attributes. The diagram represents the complete logical structure of a database.
The document provides an overview of the Entity Relationship (ER) model, which is a conceptual data modeling technique for describing and visualizing entities and their relationships. The key aspects summarized are:
- The ER model consists of entities, relationships between entities, and attributes that describe entities. Entities can be physical or conceptual objects.
- Relationships associate entities and have mapping cardinalities like one-to-one, one-to-many, many-to-one, many-to-many.
- Entities have attributes that can be simple/atomic, composite, single-valued, multi-valued, stored or derived. Primary keys uniquely identify entities.
The document discusses concepts related to conceptual database design and the entity-relationship (ER) model. It defines key concepts such as entities, attributes, entity types, entity sets, relationship sets, and ER diagram notations. It provides examples of strong and weak entities. It also explains different types of relationships, cardinality ratios, and participation constraints that can exist between entity sets in an ER diagram.
The document discusses the key features of the entity-relationship (E-R) model. The E-R model allows users to describe data in terms of objects and relationships. It provides concepts like entities, attributes, and relationships that make it easy to model real-world data. Entities represent objects, attributes describe entity features, and relationships define connections between entities. The document also discusses different types of relationships and modeling techniques like generalization, specialization, and aggregation.
1) The document describes an entity-relationship (ER) diagram for a university database. It identifies the main entities as Department, Course, Module, Lecturer, and Student.
2) The key relationships are that a Department offers multiple Courses, a Course includes multiple Modules, a Lecturer teaches multiple Modules, and a Student enrolls in a Course and takes the Modules required to complete it.
3) The document explains the different components of an ER diagram, including entities, relationships, attributes, keys, and relationship types (one-to-one, one-to-many, many-to-many). It provides examples of how to map an ER diagram to database tables.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
The document discusses data modeling and the entity-relationship model. It defines key concepts like entities, attributes, relationships, and cardinalities. Entities have attributes and can be connected through relationships. Relationships can be one-to-one, one-to-many, many-to-one, or many-to-many depending on how many entities can be associated with each other. The entity-relationship model is useful for conceptual database design and represents these concepts visually in diagrams.
The document discusses how to model a database using an entity-relationship (ER) model. It describes the key components of an ER model including entities, attributes, relationships, and keys. It explains how entities can have attributes and how relationships associate entities. It also covers mapping cardinalities, weak entities, specialization/generalization, and how to map an ER diagram to relational database tables.
Entity Relationship Diagram – ER Diagram in DBMS.pptxsukrithlal008
An Entity-Relationship (ER) diagram is a design or blueprint of a database that describes the structure of a database using a diagram. The main components of an ER diagram are entities, attributes, and relationships. An ER diagram shows the relationships among entity sets where an entity set is a group of similar entities that can have attributes. The diagram represents the complete logical structure of a database.
The document provides an overview of the Entity Relationship (ER) model, which is a conceptual data modeling technique for describing and visualizing entities and their relationships. The key aspects summarized are:
- The ER model consists of entities, relationships between entities, and attributes that describe entities. Entities can be physical or conceptual objects.
- Relationships associate entities and have mapping cardinalities like one-to-one, one-to-many, many-to-one, many-to-many.
- Entities have attributes that can be simple/atomic, composite, single-valued, multi-valued, stored or derived. Primary keys uniquely identify entities.
The document discusses concepts related to conceptual database design and the entity-relationship (ER) model. It defines key concepts such as entities, attributes, entity types, entity sets, relationship sets, and ER diagram notations. It provides examples of strong and weak entities. It also explains different types of relationships, cardinality ratios, and participation constraints that can exist between entity sets in an ER diagram.
The document discusses the key features of the entity-relationship (E-R) model. The E-R model allows users to describe data in terms of objects and relationships. It provides concepts like entities, attributes, and relationships that make it easy to model real-world data. Entities represent objects, attributes describe entity features, and relationships define connections between entities. The document also discusses different types of relationships and modeling techniques like generalization, specialization, and aggregation.
1) The document describes an entity-relationship (ER) diagram for a university database. It identifies the main entities as Department, Course, Module, Lecturer, and Student.
2) The key relationships are that a Department offers multiple Courses, a Course includes multiple Modules, a Lecturer teaches multiple Modules, and a Student enrolls in a Course and takes the Modules required to complete it.
3) The document explains the different components of an ER diagram, including entities, relationships, attributes, keys, and relationship types (one-to-one, one-to-many, many-to-many). It provides examples of how to map an ER diagram to database tables.
This document discusses the entity-relationship (ER) model for conceptual database design. It defines key concepts like entities, attributes, relationships, keys, and participation constraints. Entities can be strong or weak, and attributes can be simple, composite, multi-valued, or derived. Relationships associate entities and can specify cardinality like one-to-one, one-to-many, or many-to-many. The ER model diagrams the structure and constraints of a database before its logical and physical implementation.
The document provides an overview of conceptual database design using entity-relationship (ER) modeling. It defines key concepts in ER diagrams like entities, attributes, relationships and their cardinalities. It explains how to model different relationship types like one-to-one, one-to-many and many-to-many. It also covers advanced topics such as weak entities, generalization, specialization and aggregation. The overall purpose is to illustrate how ER diagrams can be used to design databases by visually representing the entities, attributes, and relationships in a domain.
Fundamentals of database system - Data Modeling Using the Entity-Relationshi...Mustafa Kamel Mohammadi
In this chapter you will learn
Relational data model concepts
What is entity?
What is attribute and it’s types
What is relationship?
What is an Entity-Relationship data model?
Relational data model constraints
Characteristics of relation
The document discusses the Entity Relationship (ER) model and ER diagrams. The ER model is a conceptual data modeling technique that is used to produce a database design. It displays entities, attributes, and relationships between entities. ER diagrams help visualize these components and the logical structure of databases. Some key benefits of ER diagrams include defining database terms, providing a preview of how tables connect, and allowing communication of the database structure. Common components of ER diagrams are entities, attributes, and relationships.
This document discusses entity-relationship (ER) modeling and ER diagrams. It defines key concepts such as entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also covers ER diagram notation for different types of attributes, keys, roles, and relationship cardinalities. The goal of ER modeling and diagrams is to conceptualize a database without technical details.
The document describes concepts related to entity relationship modeling including:
- Entity types represent real world objects like employees, departments, etc. and have attributes.
- Relationship types define relationships between entity types like works_for between employees and departments.
- Keys uniquely identify entities and attributes can be single/multi-valued, simple/composite.
- The example models a company database with entities for departments, projects, employees and dependents along with their attributes and relationships.
The document describes concepts related to entity relationship modeling including:
- Entity types represent real world objects like employees, departments, etc. and have attributes.
- Relationship types define relationships between entity types like works_for between employees and departments.
- An example database schema for a company is presented with entity types for departments, projects, employees, and dependents along with attributes and relationships.
- Additional concepts covered include keys, cardinality, participation constraints, and weak entity types.
This document discusses the process of database design, including conceptual modeling using entity-relationship (ER) diagrams. It begins by outlining the initial requirements gathering and conceptual modeling phases. Next, it describes logical and physical design, which involve mapping the conceptual model to relational schemas and deciding on physical storage structures. The bulk of the document then focuses on concepts in ER modeling, including entities, attributes, relationships, relationship types, weak entities, and how to represent these graphically in an ER diagram. It provides examples to illustrate key ER modeling concepts and design issues.
The document provides an introduction to SQL and database concepts. It discusses:
- What a database management system (DBMS) is and its importance for data management.
- An introduction to structured query language (SQL) as a standard language for interacting with relational databases.
- Key SQL concepts like data definition language, data manipulation language, and data control language.
- How to perform common SQL operations like creating databases and tables, inserting, updating, and deleting data, and using joins and aggregation functions.
The document provides an overview of entity relationship modeling and SQL. It defines key concepts like entities, attributes, relationships and cardinalities. It also explains how these concepts are represented in an entity relationship diagram using standard symbols like rectangles for entities and diamonds for relationships. Specific examples of one-to-one, one-to-many and many-to-one relationships are illustrated. The document aims to introduce fundamental database and data modeling principles.
This document provides an overview of entity relationship (ER) diagrams and their components. It describes ER diagrams as a way to represent the logical structure of a database using entities, attributes, and relationships. It then gives examples of different types of entities, attributes, and relationships that can be depicted in an ER diagram including weak entities, single-valued and multi-valued attributes, and one-to-one, one-to-many, many-to-one, and many-to-many relationships. Specific ER diagrams are presented for a student management system and hospital management system to further illustrate these concepts.
The document discusses the relational data model and ER model for conceptual database design. It covers key concepts such as entities, attributes, relationships, constraints, and ER diagrams. The relational data model uses tables made up of rows and columns to store data, with each table representing an entity. Relationships between entities can be one-to-one, one-to-many, many-to-one, or many-to-many. The ER model is used to design the conceptual schema and represent entities, attributes, and relationships visually using diagrams. The conceptual schema is later transformed into a logical schema for a specific database implementation.
Data modeling using the entity relationship modelJafar Nesargi
The document describes key concepts in entity relationship modeling including entity types, attributes, relationships, keys, and constraints. It provides an example database application to track employees, departments, and projects within a company. It then defines entity types for departments, projects, employees, and dependents with their attributes. It also describes relationship types, cardinalities, roles, and other modeling constructs used to design the conceptual schema.
The document discusses the process of database design and the entity-relationship (E-R) model. It covers the conceptual, logical, and physical design phases. It also explains the key concepts of the E-R model including entities, attributes, relationships, keys, and cardinality constraints. E-R diagrams provide a way to visually represent an enterprise schema using these basic E-R modeling elements and their relationships.
This document discusses data modeling using the entity-relationship model. It describes the key components of an entity-relationship model including entities, attributes, relationships, and identifiers. It explains how these components are represented graphically and provides examples of one-to-one, one-to-many, and many-to-many relationship types. It also covers topics such as weak entities, minimum and maximum cardinality, derived attributes, and how to denote optional and mandatory participation in relationships.
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
The document provides an overview of conceptual database design using the Entity-Relationship (ER) model. It describes the basic constructs of the ER model including entities, relationships, attributes, and additional features like weak entities, inheritance hierarchies, and aggregation. It also discusses modeling choices like representing concepts as entities or attributes, binary vs n-ary relationships. Constraints that can be expressed in the ER model are covered, along with the subjective nature of ER design.
The document discusses the phases of database design including requirements collection and analysis, conceptual design, and relational database schema. It provides examples of collecting requirements for a company database including entity types like department, project, and employee. It also covers conceptual modeling using an entity-relationship diagram to represent entities, attributes, relationships and constraints. Key concepts explained include entity types, attributes, relationships, cardinalities, participation constraints, weak entities, and converting a conceptual schema to a relational database schema.
The document discusses different data models including hierarchical, network, relational, and object oriented models. It also provides details on entity-relationship (E-R) modeling. The E-R model defines entities and their attributes, and relationships between entities. Key concepts include entity sets, relationship sets, mapping cardinalities, participation constraints, keys, and designing E-R diagrams. An example E-R diagram for a university management system is presented to illustrate these concepts.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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Fundamentals of database system - Data Modeling Using the Entity-Relationshi...Mustafa Kamel Mohammadi
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The document discusses the Entity Relationship (ER) model and ER diagrams. The ER model is a conceptual data modeling technique that is used to produce a database design. It displays entities, attributes, and relationships between entities. ER diagrams help visualize these components and the logical structure of databases. Some key benefits of ER diagrams include defining database terms, providing a preview of how tables connect, and allowing communication of the database structure. Common components of ER diagrams are entities, attributes, and relationships.
This document discusses entity-relationship (ER) modeling and ER diagrams. It defines key concepts such as entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also covers ER diagram notation for different types of attributes, keys, roles, and relationship cardinalities. The goal of ER modeling and diagrams is to conceptualize a database without technical details.
The document describes concepts related to entity relationship modeling including:
- Entity types represent real world objects like employees, departments, etc. and have attributes.
- Relationship types define relationships between entity types like works_for between employees and departments.
- Keys uniquely identify entities and attributes can be single/multi-valued, simple/composite.
- The example models a company database with entities for departments, projects, employees and dependents along with their attributes and relationships.
The document describes concepts related to entity relationship modeling including:
- Entity types represent real world objects like employees, departments, etc. and have attributes.
- Relationship types define relationships between entity types like works_for between employees and departments.
- An example database schema for a company is presented with entity types for departments, projects, employees, and dependents along with attributes and relationships.
- Additional concepts covered include keys, cardinality, participation constraints, and weak entity types.
This document discusses the process of database design, including conceptual modeling using entity-relationship (ER) diagrams. It begins by outlining the initial requirements gathering and conceptual modeling phases. Next, it describes logical and physical design, which involve mapping the conceptual model to relational schemas and deciding on physical storage structures. The bulk of the document then focuses on concepts in ER modeling, including entities, attributes, relationships, relationship types, weak entities, and how to represent these graphically in an ER diagram. It provides examples to illustrate key ER modeling concepts and design issues.
The document provides an introduction to SQL and database concepts. It discusses:
- What a database management system (DBMS) is and its importance for data management.
- An introduction to structured query language (SQL) as a standard language for interacting with relational databases.
- Key SQL concepts like data definition language, data manipulation language, and data control language.
- How to perform common SQL operations like creating databases and tables, inserting, updating, and deleting data, and using joins and aggregation functions.
The document provides an overview of entity relationship modeling and SQL. It defines key concepts like entities, attributes, relationships and cardinalities. It also explains how these concepts are represented in an entity relationship diagram using standard symbols like rectangles for entities and diamonds for relationships. Specific examples of one-to-one, one-to-many and many-to-one relationships are illustrated. The document aims to introduce fundamental database and data modeling principles.
This document provides an overview of entity relationship (ER) diagrams and their components. It describes ER diagrams as a way to represent the logical structure of a database using entities, attributes, and relationships. It then gives examples of different types of entities, attributes, and relationships that can be depicted in an ER diagram including weak entities, single-valued and multi-valued attributes, and one-to-one, one-to-many, many-to-one, and many-to-many relationships. Specific ER diagrams are presented for a student management system and hospital management system to further illustrate these concepts.
The document discusses the relational data model and ER model for conceptual database design. It covers key concepts such as entities, attributes, relationships, constraints, and ER diagrams. The relational data model uses tables made up of rows and columns to store data, with each table representing an entity. Relationships between entities can be one-to-one, one-to-many, many-to-one, or many-to-many. The ER model is used to design the conceptual schema and represent entities, attributes, and relationships visually using diagrams. The conceptual schema is later transformed into a logical schema for a specific database implementation.
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The document describes key concepts in entity relationship modeling including entity types, attributes, relationships, keys, and constraints. It provides an example database application to track employees, departments, and projects within a company. It then defines entity types for departments, projects, employees, and dependents with their attributes. It also describes relationship types, cardinalities, roles, and other modeling constructs used to design the conceptual schema.
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The document provides an overview of conceptual database design using the Entity-Relationship (ER) model. It describes the basic constructs of the ER model including entities, relationships, attributes, and additional features like weak entities, inheritance hierarchies, and aggregation. It also discusses modeling choices like representing concepts as entities or attributes, binary vs n-ary relationships. Constraints that can be expressed in the ER model are covered, along with the subjective nature of ER design.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
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model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
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our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
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This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
1. 1
Chapter 2: Entity-Relationship Data Model
• Conceptual Modeling of a database
• The Entity-Relationship (ER) Model
• Entity Types, Entity Sets, Attributes, and Keys, Relationship
Types, Relationship Sets, Weak Entity Types
• Generalization, Specialization and Aggregation, Extended
Entity Relationship (EER) Model.
By:- Manasi Deore
2. 2
Conceptual Modeling of a database
A data model helps to put the real world requirement into a design
Data Model is a collection of conceptual tools for describing data, data
relationships, data semantics, and consistency constraints.
It involves planning about tables, their columns, mapping between the
tables, how they are structured in the physical memory etc.
A collection of tools for describing:
Data
Data relationships
Data semantics
Data constraints
By:- Manasi Deore
5. The Entity – Relationship Model (ER model/diagram)
By:- Manasi Deore
6. The Entity – Relationship Model (ER model/diagram)
The ER data model was developed to facilitate database design by allowing
specification of an enterprise schema that represents the overall logical structure of
a database.
The E-R model is very useful in mapping the meaning and interactions of real
world enterprise onto a conceptual schema.
The E-R model employee three basic concepts: entity sets, relationship sets and
attributes.
The E-R model also has an associated diagrammatic representation: the E-R
diagram
An E-R diagram can express the overall logical structure of a database graphically.
This diagrams are simple and clear.
By:- Manasi Deore
7. Entity Sets
• A database can be modeled as:
– a collection of entities,
– relationship among entities.
• An entity is an “object” or “thing” that exists and is distinguishable from other
objects.
– Example: specific person, company, event, plant
– An entity has a set of properties and the values for some set properties
must uniquely identify an entity.
– For ex. In a SEIT class each student may have PRN_NO property whose
value uniquely identifies that student.
– Person have Person_id
– Courses can be thought of as entities , and course_id uniquely identifies a
course entity in the university.
• Entities have attributes
– Example: people have names and addresses
• An entity set is a set of entities of the same type that share the same properties.
– Example: set of all persons, companies, trees, holidays.
Customer
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8. Entity Sets customer and loan
customer-id customer- customer- customer- loan amount
name street city number
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9. Types of Entity: Strong and Weak
Strong Entity – are the entities which has a key attribute in its attribute
list or a set that has a primary key.
The strong entity type is also called regular entity type.
For Example, The Student’s unique value RollNo (Primary Key) will
identify the students.
The notation for the Strong Entity is: Rectangle
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10. Weak Entity Type – Entity Type with no key or Primary Key are
called weak entity Type. Whose existence depends on some other
strong entity.
An entity set may not have sufficient attributes to form a primary key.
The notation for the Weak Entity is: Double Rectangle
By:- Manasi Deore
11. Attributes
An entity is represented means of their properties, called attributes, that
is descriptive properties possessed by all members of an entity set.
For example, a student entity may have name, class, and age as attributes.
• Domain (value set)– the set of permitted values for each attribute .
There exists a domain or range of values that can be assigned to
attributes.
For example, a student's name cannot be a numeric value. It has to be
alphabetic. A student's age cannot be negative, etc.
Example: Student=(s_name, S_class, S_age)
customer = (customer-id, customer-name, customer-street,
customer-city)
loan = (loan-number, amount)
By:- Manasi Deore
12. Attributes Types
• Attribute types:
– Simple and composite attributes.
– Single-valued and multi-valued attributes
• E.g. multivalued attribute: phone-numbers
– Derived attributes
• Can be computed from other attributes
• E.g. age, given date of birth
Simple attribute − Simple attributes are atomic values, which cannot be
divided further.
For example, a student's phone number is an atomic value of 10 digits.
The notation for the attribute is: Ellipse
Line: It represents the link between attribute and entity
set to relationship set.
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13. The notation for the Composite or compound attribute is:
Composite attribute − can be divided into subparts(i.e. other attributed)
Composite attributes are made of more than one simple attribute.
For ex., a student's complete name may have first_name and last_name.
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14. Using composite attributes in a design schema is good choice if a user will wish to
refer to an entire attribute on some occasions, and to only a component of the
attributes on other occasions.
The address can be defined as the composite attribute address with the attributes :
street, city, state and postal_code.
Composite attributes helps us to group together related attribute, making modeling
cleaner.
Note also that composite attribute may appear as hierarchy. In the address attribute
street can be further divided into street_number, street_name and apartment_number
By:- Manasi Deore
15. Single-valued and Multivalued Attributes
Single-valued Attributes: The attributes have a single value for a particular entity.
For instant, the Roll_no attribute for specific student entity refers to only one
student ID.
Multivalued Attributes: It represents multi valued attribute which can have many
values for a particular entity.
There may be instances where an attribute has a set of values for specific entity.
For eg. A student may have zero, one or several phone numbers.
For instructor entity set an attribute dependent_name listing all dependents.
The notation for the derived attribute is:
Double ellipse
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16. Derived Attributes
These attributes are derived from other the values of other attributes or entities.
It can be derived from multiple attributes and also from a separate table.
Ex.1) Age can be derived by the difference between current date and date of birth.
In this case Date_of _birth may be referred to as a Base attribute, or a stored
attribute.
2) Assume that instructor entity set has an attribute student_advised, which
represents how may students an instructor advises. We can derive the value for this
attribute by counting the number of students associated with that instructor
3) Working hours:
(Out_time-In_time)
The notation :Dashed ellipse
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17. Relationship Sets
A relationship is an association among several entities.
Relationships are represented by diamond-shaped box.
Name of the relationship is written inside the diamond-box.
All the entities (rectangles/Double rectangles) participating in a
relationship, are connected to it by a line.
Belongs to
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18. Relationship Set: set of similar relationships
Student (entity type) is related to Department (entity type)
by MajorsIn (relationship set or entity role).
Relationship sets can have descriptive attributes like entity sets.
The Function that an entity plays in a relationship is called that entity’s role
By:- Manasi Deore
20. Relationship Sets (Cont.)
• An attribute can also be property of a relationship set.
• For instance, the depositor relationship set between entity sets customer
and account may have the attribute access-date
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21. Degree of a Relationship Set
• Refers to number of entity sets that participate in a relationship set.
• Relationship sets that involve two entity sets are binary (or degree two).
Generally, most relationship sets in a database system are binary.
• Relationship sets may involve more than two entity sets.
• Relationships between more than two entity sets are rare. Most relationships
are binary.
The number of roles in the relationship
Binary – links two entity sets; set of ordered pairs (most common)
Ternary – links three entity sets; ordered triples (rare). If a relationship exists
among the three entities, all three must be present
N-ary – links n entity sets; ordered n-tuples (very rare). If a relationship exists
among the entities, then all must be present. Cannot represent subsets.
By:- Manasi Deore
22. Degree of a Relationship Set
E.g. Suppose employees of a bank may have jobs (responsibilities) at
multiple branches, with different jobs at different branches. Then there is
a ternary relationship set between entity sets employee, job and branch.
Note: ternary relationships may sometimes be replaced by two binary
relationships. Semantic equivalence between ternary relationships and
two binary ones are not necessarily true.
By:- Manasi Deore
24. Total and Partial Participation of an Entity set
Participation Constraints
Total Participation − Each entity is involved in the relationship. Total participation
is represented by double lines.
Partial participation − Not all entities are involved in the relationship. Partial
participation is represented by single lines. some entities may not participate in any
relationship in the relationship set.
Total Participation of an Entity set:
A Total participation of an entity set represents that each entity in entity set must
have at least one relationship in a relationship set. For example: In the below
diagram each college must have at-least one associated Student.
By:- Manasi Deore
25. Participation of an Entity Set in a Relationship Set
● Total participation (indicated by double line): every entity in the entity set
participates in at least one relationship in the relationship set
● E.g. participation of loan in borrower is total
● every loan must have a customer associated to it via borrower
● Partial participation: some entities may not participate in any relationship
in the relationship set
● E.g. participation of customer in borrower is partial
By:- Manasi Deore
27. Recursive Relationship(entities can be self-linked)
If the same entity participates more than once in a relationship it is
known as a recursive relationship. In the example an employee can
be a supervisor and be supervised, so there is a recursive
relationship.
By:- Manasi Deore
28. • Entity sets of a relationship need not be distinct
• The labels “manager” and “worker” are called roles; they specify
how employee entities interact via the works-for relationship set.
• Roles are indicated in E-R diagrams by labeling the lines that
connect diamonds to rectangles.
• Role labels are optional, and are used to clarify semantics of the
relationship
By:- Manasi Deore
30. Weak Entity Sets
• An entity set that does not have a primary key is referred to as a weak entity set.
• The existence of a weak entity set depends on the existence of a identifying entity
set
– it must relate to the identifying entity set via a total, one-to-many relationship
set from the identifying to the weak entity set
– Identifying relationship depicted using a double diamond
• The discriminator (or partial key) of a weak entity set is the set of attributes that
distinguishes among all the entities of a weak entity set.
• The primary key of a weak entity set is formed by the primary key of the
strong entity set on which the weak entity set is existence dependent, plus the
weak entity set’s discriminator.
By:- Manasi Deore
31. • We depict a weak entity set by double rectangles.
• We underline the discriminator of a weak entity set with a
dashed line.
• payment-number – discriminator of the payment entity set
• Primary key for payment – (loan-number, payment-number)
By:- Manasi Deore
35. • Note: the primary key of the strong entity set is not explicitly
stored with the weak entity set, since it is implicit in the
identifying relationship.
• If loan-number were explicitly stored, payment could be made a
strong entity, but then the relationship between payment and loan
would be duplicated by an implicit relationship defined by the
attribute loan-number common to payment and loan
By:- Manasi Deore
36. • In a university, a course is a strong entity and a course-offering
can be modeled as a weak entity
• The discriminator of course-offering would be semester
(including year) and section-number (if there is more than one
section)
• If we model course-offering as a strong entity we would model
course-number as an attribute.
Then the relationship with course would be implicit in the course-
number attribute
By:- Manasi Deore
38. Mapping Cardinalities
• Cardinality ratio is also called Cardinality Mapping, which represents
the mapping of one entity set to another entity set in a relationship set.
• Most useful in describing binary relationship sets.
• Mapping Cardinality, express the number of entities to which another
entity can be associated via a relationship set.
• For a binary relationship set the mapping cardinality must be one of the
following types:
– One to one
– One to many
– Many to one
– Many to many
By:- Manasi Deore
39. One - to - One Relationship
• In One - to - One Relationship, one entity is related with only one other entity.
• One row in a table ‘A’ is linked with only one row in another table ‘B’
and vice versa.
• When only one instance of an entity is associated with the relationship, it is
marked as '1:1'.
For example: A Country can have only one Capital City.
By:- Manasi Deore
40. Alternative Notations for Cardinality Constraints
• We express cardinality constraints by drawing either a directed line (🡪),
signifying “one,” or an undirected line (—), signifying “many,” between the
relationship set and the entity set.
• E.g.: One-to-one relationship:
– A customer is associated with at most one loan via the relationship
borrower
– A loan is associated with at most one customer via borrower
By:- Manasi Deore
41. Each customer has exactly one driving license and every driving
license is associated with exactly one customer.
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42. One - to - Many Relationship
•When more than one instance of an entity is associated with a relationship i.e. one
entity is related to many other entities.
•It is marked as '1:N'.
•One row in a table A is linked to many rows in a table B, but one row in a table B
is linked to only one row in table A.
For example: One Department has many Employees.
By:- Manasi Deore
43. One-To-Many Relationship
• In the one-to-many relationship a loan is associated with at most
one customer via borrower, a customer is associated with several
(including 0) loans via borrower
By:- Manasi Deore
44. There are some employees who manage more than one team
while there is only one manager to manage a team
By:- Manasi Deore
45. Many - to - One Relationship
•When more than one instance of entity is associated with the relationship i.e. In
Many - to - One Relationship, many entities can be related with only one other
entity.
•It is marked as ‘N:1'.
•Multiple rows in Employee table(table A) is related with only one row in
Department table (table B).
•For example: No. of Employee works for Department and employee belongs to
only one department.
By:- Manasi Deore
46. Many-To-One Relationships
• In a many-to-one relationship a loan is associated with several
(including 0) customers via borrower, a customer is associated with at
most one loan via borrower
By:- Manasi Deore
47. Any number of credit cards can belong to a customer and there might be
some customers who do not have any credit card, but every credit card in a
system has to be associated with an employee(i.e. total participation).
While a single credit card can not belong to multiple customers.
By:- Manasi Deore
48. Many - to – Many Relationship
•More than one instance of an entity on the left and more than one instance of an
entity on the right can be associated with the relationship.
•It is marked as ‘N:N ‘ or ‘M:N’.
•In Many - to - Many Relationship, many entities are related with the multiple
other entities.
•For example: Various Books in a Library are issued by many Students.
By:- Manasi Deore
49. Many-To-Many Relationship
• A customer is associated with several (possibly 0) loans via
borrower
• A loan is associated with several (possibly 0) customers via
borrower
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50. A customer can buy any number of products and a product can be bought
by many customers.
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51. Keys
• A super key of an entity set is a set of one or more
attributes whose values uniquely determine each entity.
• A candidate key of an entity set is a minimal super key
– Customer-id is candidate key of customer
– account-number is candidate key of account
• Although several candidate keys may exist, one of the
candidate keys is selected to be the primary key.
By:- Manasi Deore
52. PRIMARY KEY: Uniquely identifies the record from the record .
•Though a person can be identified using his SSN, passort# or license#,
•One can choose any one of them as primary key to uniquely identify a person. Rest
of them will act as a candidate key.
•It doesn’t allow Null values.
A primary key’s main features are:
It must contain a unique value for each row of data.
It cannot contain null values.
PRIMARY KEY = UNIQUE VALUE + Not Null CONSTRAINT
By:- Manasi Deore
53. • Unique key: Allows Null value. But only one Null value.
• Foreign key: In simpler words, the foreign key is defined in a second table,
but it refers to the primary key or a unique key in the first table.
A Foreign key is a field (or collection of fields) in one table that refers to the
Primary key in another table..
Foreign key helps to establish the mapping between two or more entities.
By:- Manasi Deore
54. Keys for Relationship Sets
• The combination of primary keys of the participating entity sets forms a super
key of a relationship set.
– (customer-id, account-number) is the super key of depositor
– NOTE: this means a pair of entity sets can have at most one relationship in
a particular relationship set.
• E.g. if we wish to track all access-dates to each account by each
customer, we cannot assume a relationship for each access. We can use
a multivalued attribute though
• Must consider the mapping cardinality of the relationship set when deciding the
what are the candidate keys
• Need to consider semantics of relationship set in selecting the primary key in
case of more than one candidate key
By:- Manasi Deore
56. How to Draw ER Diagrams
Below points show how to go about creating an ER diagram.
Identify all the entities in the system. An entity should appear only
once in a particular diagram. Create rectangles for all entities and
name them properly.
Identify relationships between entities. Connect them using a line
and add a diamond in the middle describing the relationship.
Add attributes for entities. Give meaningful attribute names so they
can be understood easily.
By:- Manasi Deore
57. E-R Diagram With Composite, Multivalued, and Derived
Attributes
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59. Binary Vs. Non-Binary Relationships
• Some relationships that appear to be non-binary may be better
represented using binary relationships
– E.g. A ternary relationship parents, relating a child to
his/her father and mother, is best replaced by two binary
relationships, father and mother
• Using two binary relationships allows partial
information (e.g. only mother being know)
– But there are some relationships that are naturally non-
binary
• E.g. works-on
By:- Manasi Deore
64. By:- Manasi Deore
Database Languages
Language for accessing and manipulating the data organized by the appropriate data model.
Two classes of languages
Procedural – user specifies what data is required and how to get those data
Nonprocedural – user specifies what data is required without specifying how to get
those data
Types of DBMS languages
Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Control Language (DCL)
Transaction Control (TCL)
Data Definition Language (DDL) : Statements are used to define the database structure or
schema.
Some examples:
CREATE - to create objects in the database
ALTER - alters the structure of the database
DROP - delete objects from the database
TRUNCATE - remove all records from a table, including all spaces allocated for the records
are removed
COMMENT - add comments to the data dictionary
RENAME - rename an object
65. By:- Manasi Deore
Manipulation Language (DML) : Statements are used for managing data within schema
objects.
Some examples:
SELECT - Retrieve data from the a database
INSERT - Insert data into a table
UPDATE - Updates existing data within a table
DELETE - deletes all records from a table
Data Control Language (DCL)
Some examples:
GRANT - gives user's access privileges to database
REVOKE - withdraw access privileges given with the GRANT command
Transaction Control (TCL) : Statements are used to manage the changes made by DML
statements. It allows statements to be grouped together into logical transactions.
Some examples:
COMMIT - save work done
SAVEPOINT - identify a point in a transaction to which you can later roll back
ROLLBACK - restore database to original since the last COMMIT
SET TRANSACTION - Change transaction options like isolation level and what rollback
segment to use
66. The Enhanced Entity Relationship (EER) :
Model Enhanced ER (EER) model
• Created to design more accurate database schemas
• Reflect the data properties and constraints more precisely
• More complex requirements than traditional applications
The EER model includes all the concepts of the original E-R model
together with the following concepts:
1.Specialization
2.Generalization
•Aggregation
•Inheritance
The Enhanced Entity Relationship (EER)
67. By:- Manasi Deore
Introduction Database
System
Sub Class and Super Class
•Sub class and Super class relationship leads the concept of Inheritance.
•The relationship between sub class and super class is denoted with symbol.
1. Super Class
Super class is an entity type that has a relationship with one or more subtypes.
For example: Shape super class is having sub groups as Square, Circle, Triangle.
2. Sub Class
•Sub class is a group of entities with unique attributes.
•Sub class inherits properties and attributes from its super class.
For example: Square, Circle, Triangle
are the sub class of Shape super class.
68. Specialization
Specialization is the process of defining a set of subclasses of an entity type
Specialization is a process that defines a group entities which is divided into
sub groups based on their characteristic.
•Top-down design process: in which one higher entity can be broken down
into two lower level entity, that are distinctive from other entities in the set.
•These sub groupings become lower-level entity sets that have attributes or
participate in relationships that do not apply to the higher-level entity set.
• It maximizes the difference between the members of an entity by identifying
the unique characteristic or attributes of each member.
•It defines one or more sub class for the super class and also forms the
superclass/subclass relationship.
69. Specialization
•Depicted by a triangle component labeled ISA (E.g. customer “is a” person).
•Attribute inheritance – a lower-level entity set inherits all the attributes and
relationship participation of the higher-level entity set to which it is linked.
ISA
70. Generalization
Generalization is the process of generalizing the entities which contain
the properties of all the generalized entities.
Original entity types are special subclasses
Process of defining a generalized entity type from the given entity types
A bottom-up design process – combine a number of entity sets that share
the same features into a higher-level entity set (i.e.in which two lower
level entities combine to form a higher level entity).
Specialization and generalization are simple inversions of each other; they
are represented in an E-R diagram in the same way.
It defines a general entity type from a set of specialized entity type.
71. Generalization
It minimizes the difference between the entities by identifying the
common features.
The terms specialization and generalization are used interchangeably.
Implements Attribute inheritance.
73. By:- Manasi Deore
Let us start with the bottom. Consider three categories of Employee
FULLTIME, PARTTIME, ADHOC which have common attributes and
specialized attributes. For instance, in the above diagram, FULLTIME
employee has a special attribute designation, PARTTIME employee two
special attributes namely number of days and task, ADHOC has hours as
its special attribute. All these three sub-entities can be generalized as a
super-entity called EMPLOYEE because ultimately all three types –
fulltime, partime and adhoc are types of employees. And thereby the
common attribute will be present for EMPLOYEE – super-entity and not
the child entity since it makes more sense for a parent entity to have a
common attribute and child attributes have their own special attributes
which are not present in other entities.
75. Specialization and Generalization
• Can have multiple specializations of an entity set based on different
features.
• E.g. permanent-employee vs. temporary-employee, in addition to
officer vs. secretary vs. teller
• Each particular employee would be
– a member of one of permanent-employee or temporary-employee,
– and also a member of one of officer, secretary, or teller
• The ISA relationship also referred to as superclass - subclass
relationship
76. Design Constraints on a Specialization/Generalization
Constraint on which entities can be members of a given lower-level
entity set.
– condition-defined: If all subclasses have their membership (explicit)
condition on some attribute of superclass.
Ex. Over 65 years are members of senior-citizen entity set; senior-
citizen ISA person.
– user-defined : when we don't have predefined condition for
determining membership to a subclass.
– So we need condition determined by database user,
77. Design Constraints on a Specialization/Generalization
• Constraint on whether or not entities may belong to more than one
lower-level entity set within a single generalization.
– Disjoint
• an entity can belong to only one lower-level entity set
• Noted in E-R diagram by writing disjoint next to the ISA triangle
• Specifies that the subclasses of the specialization must be disjoint
– Overlapping
• an entity can belong to more than one lower-level entity set
80. Design Constraints on a Specialization/Generalization
Completeness constraint -- specifies whether or not an entity in
the higher-level entity set must belong to at least one of the
lower-level entity sets within a generalization.
– total : According to this constraint, each higher-level entity
must belong to a lower-level entity .
– partial: According to this constraint, higher-level entity need
not belong to one of the lower-level entity sets
81.
82. Aggregation
Drawback of ER model –cannot express relationship among
relationships.
Consider the ternary relationship works-on, which we saw earlier
Suppose we want to record managers for tasks performed by an
employee at a branch
Used to model a relationship involving a relationship set.
Allows us to treat a relationship set as an entity set for purposes of
participation in (other) relationships
83. Used to model a relationship involving a relationship set.
Allows us to treat a relationship set as an entity set for purposes
of participation in (other) relationships
84. • Relationship sets works-on and manages represent overlapping information
– Every manages relationship corresponds to a works-on relationship
– However, some works-on relationships may not correspond to any manages
relationships
• So we can’t discard the works-on relationship
• Eliminate this redundancy via aggregation
– Treat relationship as an abstract entity
– Allows relationships between relationships
– Abstraction of relationship into new entity
• Without introducing redundancy, the following diagram represents:
– An employee works on a particular job at a particular branch
– An employee, branch, job combination may have an associated manager
87. E-R Design Decisions
• The use of an attribute or entity set to represent an object.
• Whether a real-world concept is best expressed by an entity set or a
relationship set.
• The use of a ternary relationship versus a pair of binary
relationships.
• The use of a strong or weak entity set.
• The use of specialization/generalization – contributes to modularity
in the design.
• The use of aggregation – can treat the aggregate entity set as a single
unit without concern for the details of its internal structure.