The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
This document provides an overview of key concepts in entity-relationship modeling including entities, attributes, relationships, constraints, weak entities, and class hierarchies. It defines entities as objects in the real world that can be uniquely identified by a set of attributes. Relationships associate entities and can be one-to-one, one-to-many, many-to-one, or many-to-many. Constraints specify how entities can participate in relationships. Weak entities cannot be uniquely identified without attributes from a related strong entity. Class hierarchies allow entities to be classified into subclasses that inherit attributes.
Dbms lifecycle. ..Database System Development LifecycleNimrakhan89
The database development life cycle (DDLC) is a process of designing, implementing and maintaining a database system to meet strategic or operational information needs of an organisation or enterprise such as: Improved customer support and customer satisfaction. Better production management.
In this chapter, we talk about basic concepts of relational database design. We talk about the concept of functional dependency, Armstrong's axioms, closures, and minimal cover.
This chapter deals with the importance of normalization in database management systems. We learn about the necessary criterion needed for normalization. We discuss different types of normal forms along with some sample examples.
The document discusses various SQL statements and concepts. It introduces the different types of SQL statements - DQL, DML, DDL, TCL, DCL and describes common statements like SELECT, INSERT, UPDATE, DELETE. It also covers SQL concepts like data types, NULL values, joins, aggregation, sorting, filtering using WHERE clause and logical operators. Single-row functions for character, number and date manipulations are explained along with examples.
The document discusses relationship sets and the degree of a relationship set in a database management system. A relationship set is a set of relationships of the same type between two or more entity sets. The degree of a relationship set refers to the number of entity sets participating in that relationship. There are four types of relationship sets: unary, binary, ternary, and n-ary. A unary relationship involves one entity set, a binary involves two entity sets, a ternary involves three entity sets, and an n-ary relationship can involve any number of entity sets, denoted by n.
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.
This document provides an overview of database system concepts and architecture. It discusses data models, schemas, instances, and states. It also describes the three-schema architecture, data independence, DBMS languages and interfaces, database system utilities and tools, and centralized and client-server architectures. Key classification of DBMSs are also covered.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
This document discusses different types of data models, including object based models like entity relationship and object oriented models, physical models that describe how data is stored, and record based logical models. It specifically mentions hierarchical, network, and relational models as examples of record based logical data models. The purpose of data models is to represent and make data understandable by specifying rules for database construction, allowed data operations, and integrity.
The document discusses the objectives and components of the ANSI-SPARC three-level database architecture. The architecture includes an external, conceptual, and internal level. The external level defines users' views, the conceptual level defines entity relationships and constraints, and the internal level defines physical storage. Mappings allow translation between levels. The architecture aims to provide logical and physical data independence so changes to one level do not affect others.
The document discusses data modeling and different data models. It describes the evolution of data models from hierarchical to network to relational models. It also covers the entity relationship and object-oriented models. The key points are that data modeling helps reconcile different views of data, business rules inform database design, and the conceptual model provides an integrated global view of the database.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document summarizes a lecture on relational algebra and calculus. It defines relational algebra as a theoretical language used to query databases using operations that work on relations. It describes the five fundamental relational algebra operations and additional join, division, and aggregate operations. It then defines relational calculus as specifying what to retrieve from a database rather than how, and describes tuple and domain relational calculus using predicates, quantifiers, and tuple variables.
The document discusses SQL database commands including DDL commands like CREATE, ALTER, DROP and TRUNCATE used to build and modify database structures. It describes using CREATE TABLE to generate tables with data types like VARCHAR2, CHAR, NUMBER, DATE and LOB types. ALTER TABLE adds, deletes or modifies columns and constraints. DROP TABLE deletes tables. DML commands like INSERT INTO, UPDATE and DELETE are used to add, modify and remove data from tables.
The document describes shadow paging, an alternative to log-based recovery for databases. It maintains two page tables during transactions: a current page table and a shadow page table stored in non-volatile storage. This allows recovery of the pre-transaction state if needed. Shadow paging has advantages over logging like no log overhead and trivial recovery, but has higher commit overhead as many pages must be flushed. It also risks data fragmentation and requires garbage collection of old data versions after transactions.
Distributed databases allow data to be shared across a computer network while being stored on multiple machines. A distributed database management system (DDBMS) allows for the management of distributed databases and makes the distribution transparent to users. Key concepts in distributed DBMS design include fragmentation, allocation, and replication of data across multiple sites. Transparency, performance, and handling failures and concurrency are important considerations for DDBMS.
The document discusses the entity-relationship (ER) model for conceptual database design. It describes the basic constructs of the ER model including entities, attributes, relationships, keys, and various modeling choices. The ER model is useful for capturing the semantics of an application domain and producing a conceptual schema before logical and physical design.
Relational databases allow data to be stored and linked across multiple tables. This structured format makes the data more organized, avoids duplications, and enables complex queries across different aspects of the data. The key components are tables with unique identifiers, relationships between tables established through common fields, and queries to extract specific data combinations. Proper database design upfront is important to ensure the tables and relationships accurately capture and connect all the relevant entities and attributes in the study.
This document discusses database languages used in database management systems (DBMS). It describes three types of database languages: data definition language (DDL) used to define and modify the database schema; data manipulation language (DML) used to insert, update, delete and retrieve data; and data control language (DCL) used to control access privileges. Examples are provided for common statements in each language type like CREATE, ALTER, DROP for DDL and INSERT, UPDATE, DELETE, SELECT for DML. Case sensitivity and data types are also briefly covered.
Chapter-2 Database System Concepts and ArchitectureKunal Anand
This document provides an overview of database management systems concepts and architecture. It discusses different data models including hierarchical, network, relational, entity-relationship, object-oriented, and object-relational models. It also describes the 3-schema architecture with external, conceptual, and internal schemas and explains components of a DBMS including users, storage and query managers. Finally, it covers database languages like DDL, DML, and interfaces like menu-based, form-based and graphical user interfaces.
This document discusses database system concepts and architecture. It covers topics such as data models and their categories, schemas and instances, the three-schema architecture, data independence, DBMS languages and interfaces, and centralized and client-server architectures. The three-schema architecture defines three levels of schemas - internal, conceptual, and external - to support program-data independence and multiple views of the data. Data independence allows changes to lower-level schemas without affecting higher-level schemas and applications. A variety of DBMS languages exist, including DDL for schema definition and DML for data manipulation.
The document discusses the architecture of a database management system (DBMS). It describes the three levels of DBMS architecture: the external, conceptual, and internal views. The external view represents how individual users see the data. The conceptual view presents a common view of data for all users. The internal view describes the physical storage and organization of data. This three-level architecture provides data independence, where each level is isolated from changes in the other levels.
The document discusses different database models including hierarchical, network, relational, entity-relationship, object-oriented, object-relational, and semi-structured models. It provides details on the characteristics, structures, advantages and disadvantages of each model. It also includes examples and diagrams to illustrate concepts like hierarchical structure, network structure, relational schema, entity relationship diagrams, object oriented diagrams, and XML schema. The document appears to be teaching materials for a database management course that provides an overview of various database models.
Cn presentation on the topic called as re modellingg30162363
The document discusses conceptual database design using the entity-relationship model. It provides an overview of the database design process and introduces key concepts of the ER model including entities, attributes, relationships and entity types. It then presents an example database for a company (COMPANY) and develops the initial entity types and relationships for the conceptual design using ER diagram notation. The entity types include DEPARTMENT, PROJECT, EMPLOYEE and DEPENDENT and the relationship types include WORKS_ON and MANAGES.
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.
This document provides an overview of database system concepts and architecture. It discusses data models, schemas, instances, and states. It also describes the three-schema architecture, data independence, DBMS languages and interfaces, database system utilities and tools, and centralized and client-server architectures. Key classification of DBMSs are also covered.
The document presents information on Entity Relationship (ER) modeling for database design. It discusses the key concepts of ER modeling including entities, attributes, relationships and cardinalities. It also explains how to create an Entity Relationship Diagram (ERD) using standard symbols and notations. Additional features like generalization, specialization and inheritance are covered which allow ERDs to represent hierarchical relationships between entities. The presentation aims to provide an overview of ER modeling and ERDs as an important technique for conceptual database design.
This document discusses different types of data models, including object based models like entity relationship and object oriented models, physical models that describe how data is stored, and record based logical models. It specifically mentions hierarchical, network, and relational models as examples of record based logical data models. The purpose of data models is to represent and make data understandable by specifying rules for database construction, allowed data operations, and integrity.
The document discusses the objectives and components of the ANSI-SPARC three-level database architecture. The architecture includes an external, conceptual, and internal level. The external level defines users' views, the conceptual level defines entity relationships and constraints, and the internal level defines physical storage. Mappings allow translation between levels. The architecture aims to provide logical and physical data independence so changes to one level do not affect others.
The document discusses data modeling and different data models. It describes the evolution of data models from hierarchical to network to relational models. It also covers the entity relationship and object-oriented models. The key points are that data modeling helps reconcile different views of data, business rules inform database design, and the conceptual model provides an integrated global view of the database.
This document discusses different data models used in database management systems including record-based, relational, network, hierarchical, and entity-relationship models. It provides details on each model such as how data is organized. A record-based model uses fixed-length records and fields. The relational model organizes data into tables with rows and columns. The network model links entities through multiple paths in a graph structure. The hierarchical model arranges data in a tree structure. Finally, the entity-relationship model views the real world as entities and relationships between entities.
The document summarizes a lecture on relational algebra and calculus. It defines relational algebra as a theoretical language used to query databases using operations that work on relations. It describes the five fundamental relational algebra operations and additional join, division, and aggregate operations. It then defines relational calculus as specifying what to retrieve from a database rather than how, and describes tuple and domain relational calculus using predicates, quantifiers, and tuple variables.
The document discusses SQL database commands including DDL commands like CREATE, ALTER, DROP and TRUNCATE used to build and modify database structures. It describes using CREATE TABLE to generate tables with data types like VARCHAR2, CHAR, NUMBER, DATE and LOB types. ALTER TABLE adds, deletes or modifies columns and constraints. DROP TABLE deletes tables. DML commands like INSERT INTO, UPDATE and DELETE are used to add, modify and remove data from tables.
The document describes shadow paging, an alternative to log-based recovery for databases. It maintains two page tables during transactions: a current page table and a shadow page table stored in non-volatile storage. This allows recovery of the pre-transaction state if needed. Shadow paging has advantages over logging like no log overhead and trivial recovery, but has higher commit overhead as many pages must be flushed. It also risks data fragmentation and requires garbage collection of old data versions after transactions.
Distributed databases allow data to be shared across a computer network while being stored on multiple machines. A distributed database management system (DDBMS) allows for the management of distributed databases and makes the distribution transparent to users. Key concepts in distributed DBMS design include fragmentation, allocation, and replication of data across multiple sites. Transparency, performance, and handling failures and concurrency are important considerations for DDBMS.
The document discusses the entity-relationship (ER) model for conceptual database design. It describes the basic constructs of the ER model including entities, attributes, relationships, keys, and various modeling choices. The ER model is useful for capturing the semantics of an application domain and producing a conceptual schema before logical and physical design.
Relational databases allow data to be stored and linked across multiple tables. This structured format makes the data more organized, avoids duplications, and enables complex queries across different aspects of the data. The key components are tables with unique identifiers, relationships between tables established through common fields, and queries to extract specific data combinations. Proper database design upfront is important to ensure the tables and relationships accurately capture and connect all the relevant entities and attributes in the study.
This document discusses database languages used in database management systems (DBMS). It describes three types of database languages: data definition language (DDL) used to define and modify the database schema; data manipulation language (DML) used to insert, update, delete and retrieve data; and data control language (DCL) used to control access privileges. Examples are provided for common statements in each language type like CREATE, ALTER, DROP for DDL and INSERT, UPDATE, DELETE, SELECT for DML. Case sensitivity and data types are also briefly covered.
Chapter-2 Database System Concepts and ArchitectureKunal Anand
This document provides an overview of database management systems concepts and architecture. It discusses different data models including hierarchical, network, relational, entity-relationship, object-oriented, and object-relational models. It also describes the 3-schema architecture with external, conceptual, and internal schemas and explains components of a DBMS including users, storage and query managers. Finally, it covers database languages like DDL, DML, and interfaces like menu-based, form-based and graphical user interfaces.
This document discusses database system concepts and architecture. It covers topics such as data models and their categories, schemas and instances, the three-schema architecture, data independence, DBMS languages and interfaces, and centralized and client-server architectures. The three-schema architecture defines three levels of schemas - internal, conceptual, and external - to support program-data independence and multiple views of the data. Data independence allows changes to lower-level schemas without affecting higher-level schemas and applications. A variety of DBMS languages exist, including DDL for schema definition and DML for data manipulation.
The document discusses the architecture of a database management system (DBMS). It describes the three levels of DBMS architecture: the external, conceptual, and internal views. The external view represents how individual users see the data. The conceptual view presents a common view of data for all users. The internal view describes the physical storage and organization of data. This three-level architecture provides data independence, where each level is isolated from changes in the other levels.
The document discusses different database models including hierarchical, network, relational, entity-relationship, object-oriented, object-relational, and semi-structured models. It provides details on the characteristics, structures, advantages and disadvantages of each model. It also includes examples and diagrams to illustrate concepts like hierarchical structure, network structure, relational schema, entity relationship diagrams, object oriented diagrams, and XML schema. The document appears to be teaching materials for a database management course that provides an overview of various database models.
Cn presentation on the topic called as re modellingg30162363
The document discusses conceptual database design using the entity-relationship model. It provides an overview of the database design process and introduces key concepts of the ER model including entities, attributes, relationships and entity types. It then presents an example database for a company (COMPANY) and develops the initial entity types and relationships for the conceptual design using ER diagram notation. The entity types include DEPARTMENT, PROJECT, EMPLOYEE and DEPENDENT and the relationship types include WORKS_ON and MANAGES.
DATABASE DESIGNS ER DIAGRAMS REATIONA; ALGEBRAsaranyaksr92
E-R Diagrams – Enhanced-ER Model – ER-to-Relational Mapping – Functional Dependencies – Non-loss Decomposition – First, Second, Third Normal Forms, Dependency Preservation – Boyce/Codd Normal Form – Join Dependencies and fifth Normal Form
This document discusses conceptual data modeling using the entity-relationship (ER) model. It defines key concepts of the ER model including entities, attributes, relationships, entity sets, relationship sets, keys, and ER diagrams. It explains how the ER model is used in the early conceptual design phase of database design to capture the essential data requirements and produce a conceptual schema that can be later mapped to a logical and physical database implementation.
The document discusses the conceptual design of a database for a company. It includes entity types such as departments, projects, employees and dependents. Relationship types such as works for, manages, controls and works on are identified. An ER diagram is presented showing the entity types and relationship types. Weak entity types and constraints on relationships are also explained.
The document discusses database design processes and concepts. It covers:
1) The objectives of database design are to create logical and physical models of the proposed database system. The logical model focuses on data requirements while the physical model translates the logical design based on hardware/software constraints.
2) Proper database design is important as it provides a blueprint for how data is stored and accessed, defines application behavior, and meets user requirements. It can also improve performance.
3) The overall workflow involves requirement analysis, database designing including logical and physical models, and implementation including testing to ensure requirements are met.
Introduction to Database Management Systems Reem Sherif
The document provides an introduction and overview of database management systems (DBMS) including basic concepts, structured query language (SQL), and non-SQL databases. It outlines a course agenda covering these topics over two days, and then delves into explanations of key concepts such as the file-based system approach and its limitations, definitions of database terminology, database users, database system architecture, data models, and entity relationship modeling. Examples are also provided to illustrate database design and entity relationship diagrams.
The document discusses key concepts of data modeling including entity types, attributes, relationships, and keys. It provides examples of how to represent these concepts in an entity-relationship (ER) diagram including using rectangles for entities, ellipses for attributes, and diamonds for relationships. It also discusses different types of entities, attributes, relationships, keys and provides notation for drawing ER diagrams.
The document discusses conceptual data modeling using the entity-relationship (ER) model. It begins by explaining how database designers interview users to understand data requirements and create a conceptual schema using a high-level ER model. This conceptual schema is then transformed into an implementation model using a commercial database system. The document also provides examples of an entity, attribute, relationship, and developing an ER diagram for a sample company database that tracks employees, departments, and projects.
Modern Database Management: Principles, Practices, and Applicationsusarwar382
A database is an organized collection of structured information, typically stored electronically in a computer system. It allows for efficient storage, retrieval, and management of data. Databases are managed using Database Management Systems (DBMS), which provide tools for creating, querying, updating, and administering the data. Common types include relational databases, which use tables and SQL for queries, and NoSQL databases, which are more flexible in handling unstructured data. Databases are crucial for applications in various fields, ensuring data consistency, security, and scalability. for more information visit site https://www.schemadesign.com/
The document discusses entity relationship (ER) modeling and database design. It covers collecting requirements, conceptual design, logical design, and physical design. Key aspects of ER modeling are explained, including entities, attributes, relationships, entity types, keys, and conceptual design. The conceptual design shown models entities such as company, department, employee, and their relationships.
The document discusses different types of data models including conceptual, physical, and implementation models. It describes key aspects of data models such as their structure, constraints, and operations. Specific models covered include the entity-relationship model, network model, object-oriented model, and relational model. Key components of the entity-relationship model like entities, attributes, relationships, and ER diagrams are defined. The network and object-oriented models are also briefly explained.
The document discusses conceptual data modeling using entity-relationship (ER) models. It defines key concepts in ER modeling such as entities, attributes, relationships, cardinalities, and participation constraints. Entities can have attributes and relationships with other entities. Relationships have cardinality constraints that specify how many entities can participate in a relationship, such as one-to-one, one-to-many, or many-to-many. Participation constraints specify whether an entity's participation in a relationship is mandatory or optional. Together, cardinalities and participation constraints specify the structural constraints of relationships in an ER model.
ER diagram slides for datanase stujdy-1.pdfSadiaSharmin40
The document discusses database schema design using the entity-relationship (ER) model. It describes the database design process, which involves requirements analysis, conceptual design, and implementation including logical and physical design. The conceptual design phase develops a high-level description of the database using a technique like ER modeling. ER modeling represents entities, entity sets, attributes, relationships, and keys graphically. Relationships associate entities and define how they are related. The conceptual schema and functional requirements are then implemented through logical and physical database design.
The document provides an overview of data modeling and conceptual data modeling. It discusses key concepts in data modeling including entity relationship diagrams, attributes, domains, entity types, weak vs strong entities, and entity sets. It explains how data modeling follows analysis and documents business rules and policies to design a conceptual model of the database and relationships between data. The conceptual model is represented using an ERD.
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.
Chapter-3 Data Modeling Using the Entity-Relationship ModelRaj vardhan
The document describes conceptual database design using the entity-relationship (ER) model. It discusses key concepts of the ER model including entities, attributes, relationships, and relationship constraints. An example ER diagram is presented for a COMPANY database with entities for employees, departments, and projects. Relationships include employees working for departments and departments controlling projects. The summary provides an overview of the important concepts and examples covered in the document related to conceptual database design using the ER model.
In modern aerospace engineering, uncertainty is not an inconvenience — it is a defining feature. Lightweight structures, composite materials, and tight performance margins demand a deeper understanding of how variability in material properties, geometry, and boundary conditions affects dynamic response. This keynote presentation tackles the grand challenge: how can we model, quantify, and interpret uncertainty in structural dynamics while preserving physical insight?
This talk reflects over two decades of research at the intersection of structural mechanics, stochastic modelling, and computational dynamics. Rather than adopting black-box probabilistic methods that obscure interpretation, the approaches outlined here are rooted in engineering-first thinking — anchored in modal analysis, physical realism, and practical implementation within standard finite element frameworks.
The talk is structured around three major pillars:
1. Parametric Uncertainty via Random Eigenvalue Problems
* Analytical and asymptotic methods are introduced to compute statistics of natural frequencies and mode shapes.
* Key insight: eigenvalue sensitivity depends on spectral gaps — a critical factor for systems with clustered modes (e.g., turbine blades, panels).
2. Parametric Uncertainty in Dynamic Response using Modal Projection
* Spectral function-based representations are presented as a frequency-adaptive alternative to classical stochastic expansions.
* Efficient Galerkin projection techniques handle high-dimensional random fields while retaining mode-wise physical meaning.
3. Nonparametric Uncertainty using Random Matrix Theory
* When system parameters are unknown or unmeasurable, Wishart-distributed random matrices offer a principled way to encode uncertainty.
* A reduced-order implementation connects this theory to real-world systems — including experimental validations with vibrating plates and large-scale aerospace structures.
Across all topics, the focus is on reduced computational cost, physical interpretability, and direct applicability to aerospace problems.
The final section outlines current integration with FE tools (e.g., ANSYS, NASTRAN) and ongoing research into nonlinear extensions, digital twin frameworks, and uncertainty-informed design.
Whether you're a researcher, simulation engineer, or design analyst, this presentation offers a cohesive, physics-based roadmap to quantify what we don't know — and to do so responsibly.
Key words
Stochastic Dynamics, Structural Uncertainty, Aerospace Structures, Uncertainty Quantification, Random Matrix Theory, Modal Analysis, Spectral Methods, Engineering Mechanics, Finite Element Uncertainty, Wishart Distribution, Parametric Uncertainty, Nonparametric Modelling, Eigenvalue Problems, Reduced Order Modelling, ASME SSDM2025
Input to Code Generator
Output of Code Generator
Memory Management
Instruction Selection
Register Allocation
Evaluation Order
Approaches to code generation
The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like Blast furnace slag (GGBFS), fly ash, metakaolin, silica fume can be used as partly replacement for cement and manufactured sand obtained from crusher, was partly used as fine aggregate. In this work, MATLAB software model is developed using neural network toolbox to predict the flexural strength of concrete made by using pozzolanic materials and partly replacing natural fine aggregate (NFA) by Manufactured sand (MS). Flexural strength was experimentally calculated by casting beams specimens and results obtained from experiment were used to develop the artificial neural network (ANN) model. Total 131 results values were used to modeling formation and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. 25 input materials properties were used to find the 28 days flexural strength of concrete obtained from partly replacing cement with pozzolans and partly replacing natural fine aggregate (NFA) by manufactured sand (MS). The results obtained from ANN model provides very strong accuracy to predict flexural strength of concrete obtained from partly replacing cement with pozzolans and natural fine aggregate (NFA) by manufactured sand.
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
https://www.infopitaara.in/
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
Dear SICPA Team,
Please find attached a document outlining my professional background and experience.
I remain at your disposal should you have any questions or require further information.
Best regards,
Fabien Keller
Reese McCrary_ The Role of Perseverance in Engineering Success.pdfReese McCrary
Furthermore, perseverance in engineering goes hand in hand with ongoing professional growth. The best engineers never stop learning. Whether improving technical skills or learning new software tools, they understand that innovation doesn’t stop with completing one project. They habitually stay current with the latest advancements, seeking continuous improvement and refining their expertise.
Relations and Functions – Understanding the Foundation of Mathematics.pptxsrmvalliammaicse2
Title: Relations and Functions – Understanding the Foundation of Mathematics
This presentation provides a comprehensive overview of relations and functions, which are core concepts in algebra and essential tools in various fields such as computer science, physics, engineering, and economics. The goal is to help students understand how data and variables interact through rules and mappings.
We begin with the definition of a relation, explaining it as a set of ordered pairs that connects elements from one set (domain) to another (codomain). The presentation covers different ways to represent relations—through arrow diagrams, tables, graphs, and set notation. It also highlights important types of relations: reflexive, symmetric, transitive, and equivalence relations, with clear examples to illustrate each type.
Moving on to functions, we explore the idea of a special kind of relation in which every input has exactly one output. The presentation explains the criteria that distinguish a function from a general relation and elaborates on various types of functions such as linear, quadratic, polynomial, exponential, logarithmic, and piecewise functions. Special attention is given to domain and range, how to determine them, and their importance in real-world scenarios.
Graphical interpretation plays a significant role in this presentation. We illustrate how graphs of functions help in visualizing behavior, trends, and continuity. The Vertical Line Test is explained as a method to identify whether a graph represents a function. The concept of one-to-one functions and inverse functions is introduced, with examples showing how to find inverses algebraically and graphically.
To bridge theory with application, the presentation includes practical examples where relations and functions are used, such as tracking population growth, measuring speed over time, and modeling financial interest.
Finally, the presentation ends with a recap of key differences between relations and functions, a summary of important properties, and a few interactive problems to test understanding.
This PowerPoint is designed to not only provide theoretical knowledge but also foster critical thinking and real-world application of mathematical concepts related to relations and functions.
YJIT can make Ruby code run faster, but this is a balancing act, because the JIT compiler itself must consume both memory and CPU cycles to compile and optimize your code while it is running. Furthermore, in large-scale production environments such as those of GitHub, Shopify and Stripe, we end up in a situation where YJIT is compiling the same code over and over again on a very large number of servers, which seems very inefficient.
In this presentation, we will go over the design of ZJIT, a next generation Ruby JIT which aims to save and reuse compiled code between executions. We hope that this will help us eliminate duplicated work while also allowing the compiler to spend more time optimizing code so that we can get better performance.
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Ad
Chapter-3 Data Modeling using ER Model
1. Learning Resource
On
Database Management Systems
Chapter-3
Data Modeling using Entity Relationship Model
Prepared By:
Kunal Anand, Asst. Professor
SCE, KIIT, DU, Bhubaneswar-24
2. Chapter Outcome:
• After the completion of this chapter, the students
will be able to:
– Explain different phases of Database Design
– Describe several terminologies associated with
ER Model
– Describe different relationship types, its
cardinality, and constraints.
– Draw ER diagram for any database application
– List out design issues in ER Model
16 March 2021 2
3. Organization of this Chapter:
• Introduction
• Phases of Database Design
• A Sample Database Application
• Entity types, Entity, Entity sets, and Attributes
• Relationship types, Relationship sets, Roles and
Structure Constraints
• ER Diagrams, Naming Conventions, Design issues
• Exercise problems for practice.
16 March 2021 3
4. Introduction
• A Database application refers to a particular database and
associated programs that implement the database queries and
updates.
– For Example: A bank database application keeps track of
customer accounts using database updates corresponding to
customer withdrawals and deposits.
• These programs provide a user friendly interface like menu
based, form based, and GUI to its users so that they can
perform the operations to meet their requirements.
• Hence, a part of database application also require the design,
implementation, and testing of these programs.
16 March 2021 4
6. Phases of Database Design (contd..)
• Requirement Collection and Analysis
– During this phase, the database designer talks to all the
possible users to understand and document their
requirements.
– These requirements are analyzed in detail to specify the
data and functional requirements for the users.
– This must be done very carefully as all the future phases
depend on the correctness of this phase.
• Conceptual Design
– Next, by applying a high level data model like ER Model,
the designer translates these requirements into a conceptual
schema of the database.
16 March 2021 6
7. Phases of Database Design (contd..)
– The designer reviews the schema to confirm that all data
requirements are satisfied and are not in conflict with one
another.
• Logical Design
– The next step, known as logical design, is the actual
implementation of the database using any DBMS.
– Here, using an implementational data model like relational
model, conceptual schema is transformed into logical
schema. This is known as Data Model Mapping.
16 March 2021 7
8. Phases of Database Design (contd..)
• Internal Design
– Once the logical schema is obtained the last phase, known
as internal design, is performed.
– During this phase, the internal storage structures, file
organizations, indexes, access paths, and physical design
parameters for the database files are specified.
• Development of Application Programs
– In parallel with these activities, application programs are
designed and implemented.
Note: The phases before logical design are DBMS
independent whereas, the afterward phases are DBMS
specific.
16 March 2021 8
9. Sample Database Application
• A database application, called COMPANY, keeps track of its
EMPLOYEEs, DEPARTMENTs, and PROJECTs.
• After the requirement collection and analysis phase, the
designers came up with the following description of the
database COMPANY.
– The company is organized into departments. Each
department has a unique name, a unique number, and an
employee that manages the department.
– A department controls a number of projects, each of which
has a unique name, unique number, and a location.
– The database stores each Employee Name, Employee ID,
SSN, age, address, gender, salary, DOB, and supervisor.
16 March 2021 9
10. Sample Database Application
– An employee is assigned to one department, but may work
on multiple projects, which are not necessarily controlled
by the same department.
– The database also keeps the track of current number of
hours per week that an employee works on each project,
along with the direct supervisor of each employee.
– The database also records the dependents of each employee
for insurance purpose, including each dependent's first
name, gender, DOB, and relationship to the employee.
16 March 2021 10
11. Entity Type and Attribute
• Entity Type
– It is a basic object represented in a ER model.
– An entity type represents any real world object that can be
represented in a database.
• Attributes
– Each entity type can be described into a database with the
help of some features or properties or characteristics known
as attributes.
• For example: EMPLOYEE is an entity type with
attributes like employee's name, Emp_ID, SSN, address,
gender, salary, and DOB.
– A particular entity in an entity type will have value for each
of its attributes.
16 March 2021 11
12. Types of Attributes
• In ER Model, following types of attributes exist:
– Simple versus Composite: Attributes that can't be sub-
divided further are known as simple attribute, whereas the
composite attributes can be sub-divided further.
• Ex: “SSN” attribute can't be sub-divided further
whereas “Address” attribute of EMPLOYEE can be
divided further into Street_address, City, State, and PIN.
– Single-valued versus Multi-valued: Attribute which have
single value for a particular entity is known as single-
valued attribute, whereas attribute which may have a set of
values for the same entity is known as multi-valued
attribute.
• Ex: “Age” is a single-valued attribute whereas “Mobile
Number” is a multi-valued attribute.
16 March 2021 12
13. contd..
• Stored versus Derived: Attribute for which the values are
directly stored, is known as stored attribute. Similarly, there
are attributes for which direct values can not be stored, rather
the values are derived from the stored attributes. They are
known as derived attributes.
– For ex: “DOB” is the attribute that will be stored directly
for an employee. On the other hand, “Age” is a derived
attribute as it will be derived from the DOB attribute.
• NULL Values:
• In some cases, a particular entity may not have any
applicable value for an attribute. In this situation, a special
value NULL is created. It can also be used when the value
of an attribute is unknown.
16 March 2021 13
14. contd..
» “Degree” is an attribute that only applies to those employee
who has a degree. There may be employees without a
degree. In this case, NULL value will be stored for them
against the attribute “Degree”.
• Complex Attribute: Composite and multi-valued attributes
can be nested for grouping components of a composite
attribute between ( ) with commas, and by displaying multi-
valued attributes between { }. Such attributes are known as
complex attributes.
– Ex:{Address_Phone({Phone(Area_code,Phone_number)},
Address(Street_address(Street_Name,Apartment_Number),
City,State,PIN))}
16 March 2021 14
15. Entity Types and Entity Sets
• Entity Type
– It is a collection of entities that have the same attributes.
However, each entity has its own value for each attribute.
– In a database, each entity type is represented by its name
and attributes.
– Ex: EMPLOYEE is an entity type with attributes Emp_ID,
Name, Age, Salary, etc.
• Entity Set
– The collection of all entities of a particular entity type in
the database at any point of time is referred to as entity set.
– Entity set is also referred by the same name as the entity
type.
16 March 2021 15
16. contd..
• Entity type and entity set are
represented here.
• An entity type is represented
in ER diagram as a
“Rectangular Box” enclosing
the entity type name.
• Attributes are enclosed in
ovals and are attached to
their entity with straight
lines.
16 March 2021 16
17. contd..
• Composite attributes are
connected to their
components by straight
lines.
• Multi-valued attributes
are displayed using double
oval.
• Derived attribute is
represented as dashed oval.
• A STUDENT entity type is
shown here along with its
attributes.
16 March 2021 17
18. Key Attribute of an Entity Type
• An entity type usually have one or more attributes whose
values are distinct for each individual entity in the entity set.
Such attribute is known as Key Attribute.
• Key attributes can be used to identify each entity uniquely.
– For example: Consider an entity type CAR with attributes
like Model, Make, Color, Year, Reg., Vehicle_ID. Here,
Reg., and Vehicle_ID can be the key attribute.
• In ER diagram, each key attribute has its name underlined
inside the oval.
• No key attribute for two individual entity from an entity set
can have same value as key attribute uniquely identifies each
entity.
16 March 2021 18
20. Value Set or Domain of an Attribute
16 March 2021 20
• Each simple attribute of an entity type is associated with a
value set that specifies the set of values which may be assigned
to that attribute for each individual entity.
• For ex:
– Age attribute of an entity type EMPLOYEE may have the
range between 21 to 65. Hence, the value set here is 21 to
65 for the attribute Age.
– Similarly, DOJ or DOB is an attribute where the values
will be given from a pre defined set like {Day, Month,
Year}. Hence, the value set can be {(1-31),(1-12),(1950-
2020)}.
• Value set is not displayed in the ER diagram and are typically
specified using the basic data type available in most
programming language.
21. Initial Conceptual Design of COMPANY
database
• Based on the requirements discussed in our sample problem,
following entity types can be defined along with their
attributes.
• EMPLOYEE (Entity Type)
– Attribute: Emp_name, Emp_ID, SSN, age, address,
gender, salary, DOB, and supervisor
• DEPARTMENT (Entity Type)
– Attribute: Name, Number, Location, Manager, and
Manager_start_date
• PROJECT (Entity Type)
– Attribute: Name, Number, Location, Controlling_dept
• DEPENDENT (Entity Type)
– Attribute: Name, gender, DOB, Employee details, Relationship
16 March 2021 21
23. Relationship Types, Relationship Sets, Roles,
and Structural Constraints
• Multiple entity types are associated to each other in some form
of relationship. This association is known as relationship
type. The relationship name is a verb.
• A relationship set is a set of relationship instances of the same
type. Relationship sets are represented by diamonds and are
connected to the participant entity sets.
16 March 2021 23
24. contd..
• A relationship type R among
n entity types e1, e2,
e3,....,en defines a set of
relationship set among
entities.
– For ex: EMPLOYEE
works for
DEPARTMENT; here,
the entity type
EMPLOYEE is
associated with the entity
type DEPARTMENT and
the relationship type is
“Works for”.
16 March 2021 24
25. contd..
• Each relationship instance ri is connected to the EMPLOYEE
and DEPARTMENT entity types.
• The employees e1,e3, and e6 work for department d1; the
employees e2 and e4 works for department d2; and the
employees e5 and e7 work for department d3.
• The degree of a relationship type is the number of entity types
that participate in a relationship. For example: The degree of
“Works for” relationship type is 2.
• A relationship type of degree 2 is called binary relationship
whereas, the relationship type of degree 3 is known as ternary
relationship.
16 March 2021 25
26. contd..
• Each entity type participating in a relationship type plays a
role in that relationship.
– For ex: In works for relationship EMPLOYEE plays the
role of worker and DEPARTMENT plays the role of
employer.
• Recursive Relationship: Role names are significant where the
same entity type participates more than once in a relationship,
but in different roles. Such relationship types are known as
recursive relationship or self referencing relationship.
– For ex: each employee has a supervisor which is again an
employee i.e. The EMPLOYEE entity type is associated to
this “supervises” relationship in two roles, one as a
supervisor and another as Subordinate.
16 March 2021 26
27. Constraints in Relationship
• Mapping Constraint or Cardinality: The number of times an
entity of an entity set participates in a relationship set is known
as cardinality. Cardinality can be of different types:
– One to One (1:1): When each entity in each entity set can
take part only once in the relationship, the cardinality is one
to one.
16 March 2021 27
28. contd..
• Many to one (M:1) When entities in one entity set can take
part only once in the relationship set and entities in other entity
set can take part more than once in the relationship set,
cardinality is many to one.
– Let us assume that a student can take only one course but one
course can be taken by many students. So the cardinality will be n
to 1.
16 March 2021 28
29. contd..
• Many to many (M:N) – When entities in all entity sets can
take part more than once in the relationship cardinality is many
to many.
– Let us assume that a student can take more than one course and one
course can be taken by many students. So the relationship will be
many to many.
16 March 2021 29
30. contd..
• One to Many (1:M)- An entity in A is associated with any
number (zero or more) of entities in B; an entity in B,
however, is associated with no more than 1 entity of A.
– For example: A customer can have multiple loans.
However, a loan can only have one customer. Hence, its a
1:M relationship.
16 March 2021 30
31. Participation Constraints
• Participation Constraint is applied on the entity participating in
the relationship set.
• Total Participation: Each entity in the entity set must
participate in the relationship. If each student must enroll in a
course, the participation of student will be total. Total
participation is shown by “double line” in ER diagram.
• Partial Participation: The entity in the entity set may or may
not participate in the relationship. If some courses are not
enrolled by any of the student, the participation of course will
be partial. It is shown by “single line”.
– The diagram depicts the ‘Enrolled in’ relationship set with
STUDENT Entity set having total participation and
COURSE Entity set having partial participation.
16 March 2021 31
33. Strong and Weak Entity Type
• An entity type that has its own KEY attribute, is known as
strong entity type.
– Ex: In COMPANY database, the entity types
EMPLOYEE, DEPARTMENT, and PROJECT are
strong as they do have their own key attribute.
• On the other hand, Weak entity type does not have their own
KEY attribute.
– Ex: In COMPANY database, the DEPENDENT entity
type is a weak entity type as it does not have a key attribute
of its own. It has a partial key which may be used to
uniquely identify the weak entity that are related to the
same owner entity.
• In ER diagram, strong entity type is represented using a single
line rectangular box, whereas weak entity type is represented
using double line rectangular box.
16 March 2021 33
34. contd..
• For a weak entity set to be meaningful, it must be associated
with another strong entity set called identifying or owner
entity set.
• The relationship associating the weak entity set with the
identifying entity set is called the identifying relationship.
• The identifying relationship is usually many-to-one and the
participation of the weak entity set in the relationship is total
participation.
– Ex: DEPENDENT entity type is related to the
EMPLOYEE entity type using M:1 relationship type and
the participation of the weak entity set is total.
16 March 2021 34
35. Refined Conceptual Design for COMPANY
database
• Now, the refined ER design for the COMPANY database is
below:
– MANAGES:
• Cardinality: 1:1
• Entity types: EMPLOYEE and DEPARTMENT
• Participation: EMPLOYEE (Partial); DEPARTMENT (Total)
– WORKS_FOR:
• Cardinality: N:1
• Entity types: EMPLOYEE and DEPARTMENT
• Participation: EMPLOYEE (Total); DEPARTMENT (Total)
– CONTROLS:
• Cardinality: 1:N
• Entity types: DEPARTMENT and PROJECT
• Participation: DEPARTMENT (Partial); PROJECT (Total)
16 March 2021 35
38. ER Diagram Notations
• In ER diagram, the
emphasis is on to represent
the schema rather than
instances.
• This is because the schema
changes rarely but the
instances keep on changing.
• The different symbols used
in ER diagram is as shown
in this diagram.
16 March 2021 38
39. Naming Conventions
– The naming conventions used in a ER diagram is as below:
• Entity types are represented in uppercase letters.
• Attributes are written using Initial letter in uppercase and
remaining in lowercase.
• Roles of entity types in a relationship type is written in
lowercase letters.
• Relationship types are written using verbs.
• Binary relationship is readable from left to right or top to
bottom.
16 March 2021 39
40. ER Design Methodology
• The guidelines that should be followed while designing an ER
diagram are discussed below:
•Recognize entity types
•Recognize relationship types and participating entity types.
•Recognize attributes of entity type and attributes of
relationship types.
•Define binary relationship types and existence
dependencies.
•Define general cardinality, constraints, keys, and
discriminators (for weak entity type).
•Design diagram
16 March 2021 40
41. Design Issues in ER Model
• The ER model usually faces following challenges:
– Use of Entity set vs. Attributes: In the real world
situations, sometimes it is difficult to select the property as
an attribute or an entity set.
• For ex: In EMPLOYEE entity type, telephone can be an attribute,
but the same can be represented as a separate entity type named
TELEPHONE.
16 March 2021 41
42. contd..
– Use of Entity sets vs. Relationship sets: Sometimes, an
entity set can be better expressed in relationship set. Thus,
it is not always clear whether an object is best expressed by
an entity set or a relationship set.
16 March 2021 42
43. contd..
– Binary vs. n-ary relationship sets: Relationships in databases
are often binary. Some relationships that appear to be non-binary
could actually be better represented by several binary
relationships.
16 March 2021 43
44. contd..
• Placement of Relationship Attributes: The cardinality ratio
of a relationship can affect the placement of relationship
attributes.
•One-to-Many: Attributes of 1:M relationship set can be
repositioned to only the entity set on the many side of the
relationship.
•One-to-One: The relationship attribute can be associated with
either one of the participating entities
•Many-to-Many: Here, the relationship attributes can not be
represented to the entity sets; rather they will be represented
by the entity set to be created for the relationship set.
16 March 2021 44
45. The Flight Database
• The Flight database stores detail about an airline’s fleet,
flights, and seat bookings.
• Consider the following requirements list:
– The airline has one or more airplanes.
– An airplane has a model number, a unique registration
number, and the capacity to take one or more passengers.
– An airplane flight has a unique flight number, a departure
airport, a destination airport, a departure date and time, and
an arrival date and time.
– Each flight is carried out by a single airplane.
– A passenger has given names, a surname, and a unique
email address.
– A passenger can book a seat on a flight.
16 March 2021 45
49. Exercise Problems for Practice
• Please refer “Fundamentals of Database Systems” by Navathe
for the given problems.
– Please refer UNIVERSITY database application from
section 3.10; pp. 92-94
– Please go through the Q. 3.23 BANK database application;
pp. 99 and answer the questions.
– Repeat the questions of 3.23 for the ER diagram of “Airline
database schema”; pp. 98.
– Refer the Q. 3.28 MOVIE database and answer the
questions by referring to the given ER diagram; pp. 101
– Draw the ER diagrams for the Q. 3.32, Q. 3.33, and Q.
3.34; pp. 103-104.
16 March 2021 49