The document discusses the relational database model. It was introduced in 1970 and became popular due to its simplicity and mathematical foundation. The model represents data as relations (tables) with rows (tuples) and columns (attributes). Keys such as primary keys and foreign keys help define relationships between tables and enforce integrity constraints. The relational model provides a standardized way of structuring data through its use of relations, attributes, tuples and keys.
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
Multimedia refers to using multiple media types like text, audio, graphics, video and animations together. The document discusses the importance of multimedia in classrooms and its benefits. It provides details on the different elements of multimedia, types of multimedia devices, stages of multimedia production, advantages and disadvantages of using multimedia. In conclusion, the use of multimedia engages students, improves learning outcomes and develops important skills.
Artificial intelligence (AI) refers to simulating human intelligence through machine learning and programming. AI has the potential to revolutionize many industries including healthcare. Some ways AI is used in healthcare include medical diagnosis using images, personalized medicine through genetic and medical history analysis, drug discovery by analyzing trial data, clinical decision support, chronic disease management, predictive analytics, and population health management. The advantages of AI in healthcare include increased efficiency, improved accuracy, enhanced decision making through large data analysis, and potential improved outcomes. Challenges include concerns about accuracy and bias in training data and ethical issues in its use.
This document discusses software reliability models and the Rayleigh model in particular. It explains that reliability models can be static or dynamic, and the Rayleigh model is a dynamic model based on a Weibull distribution. The Rayleigh model uses parameters estimated from project data to project defect rates. Higher defect rates during development generally correlate with higher field defect rates. More defects found and removed earlier in the process yields better quality. Accuracy of models depends on valid input data and establishing predictive validity for different organizations.
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
Normalisation is a process that structures data in a relational database to minimize duplication and redundancy while preserving information. It aims to ensure data is structured efficiently and consistently through multiple forms. The stages of normalization include first normal form (1NF), second normal form (2NF), third normal form (3NF), Boyce-Codd normal form (BCNF), fourth normal form (4NF) and fifth normal form (5NF). Higher normal forms eliminate more types of dependencies to optimize the database structure.
This document provides an overview of relational database management systems (RDBMS). It defines key database concepts like data, information, and database systems. It also explains the hierarchical structure of DBMS and compares flat file databases to relational databases. Relational databases incorporate multiple normalized tables that can be related to each other, while flat files put all data in a single table without relationships between files.
Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Normalization is the process of removing redundant data from your tables to improve storage efficiency, data integrity, and scalability.
Normalization generally involves splitting existing tables into multiple ones, which must be re-joined or linked each time a query is issued.
Why normalization?
The relation derived from the user view or data store will most likely be unnormalized.
The problem usually happens when an existing system uses unstructured file, e.g. in MS Excel.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This document provides an overview of Boyce-Codd normal form (BCNF) which is a type of database normalization. It explains that BCNF was developed in 1974 and aims to eliminate redundant data and ensure data dependencies make logical sense. The document outlines the five normal forms including 1NF, 2NF, 3NF, BCNF, and 4NF. It provides examples of converting non-BCNF tables into BCNF by identifying and removing overlapping candidate keys and grouping remaining items into separate tables based on functional dependencies.
Functional dependencies (FDs) describe relationships between attributes in a database relation. FDs constrain the values that can appear across attributes for each tuple. They are used to define database normalization forms.
Some examples of FDs are: student ID determines student name and birthdate; sport name determines sport type; student ID and sport name determine hours practiced per week.
FDs can be trivial, non-trivial, multi-valued, or transitive. Armstrong's axioms provide rules for inferring new FDs. The closure of a set of attributes includes all attributes functionally determined by that set according to the FDs. Closures are used to identify keys, prime attributes, and equivalence of FDs.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
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.
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
The document discusses various SQL concepts like views, triggers, functions, indexes, joins, and stored procedures. Views are virtual tables created by joining real tables, and can be updated, modified or dropped. Triggers automatically run code when data is inserted, updated or deleted from a table. Functions allow reusable code and improve clarity. Indexes allow faster data retrieval. Joins combine data from different tables. Stored procedures preserve data integrity.
The document provides an overview of the relational model and relational algebra used in relational databases. It defines key concepts like relations, tuples, attributes, domains, schemas, instances, keys, and normal forms. It also explains the six basic relational algebra operations - select, project, union, difference, cartesian product, and rename - and how they can be composed to form complex queries. Examples of relations and queries involving operations like selection, projection, joins are provided to illustrate relational algebra.
4 the relational data model and relational database constraintsKumar
The document discusses the relational data model and constraints in relational databases. It begins by defining key concepts in the relational model such as relations, tuples, attributes, domains and relation schemas. It then covers relational constraints including key constraints, entity integrity constraints, and referential integrity constraints. Examples are provided to illustrate these concepts and constraints. The chapter aims to provide an overview of the formal relational model and constraints that must hold in relational databases.
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
A data model is a set of concepts that define the structure of data in a database. The three main types of data models are the hierarchical model, network model, and relational model. The hierarchical model uses a tree structure with parent-child relationships, while the network model allows many-to-many relationships but is more complex. The relational model - which underlies most modern databases - uses tables with rows and columns to represent data, and relationships are represented by values in columns.
The document provides an overview of databases and database design. It defines what a database is, what databases do, and the components of database systems and applications. It discusses the database design process, including identifying fields, tables, keys, and relationships between tables. The document also covers database modeling techniques, normalization to eliminate redundant or inefficient data storage, and functional dependencies as constraints on attribute values.
Normalisation is a process that structures data in a relational database to minimize duplication and redundancy while preserving information. It aims to ensure data is structured efficiently and consistently through multiple forms. The stages of normalization include first normal form (1NF), second normal form (2NF), third normal form (3NF), Boyce-Codd normal form (BCNF), fourth normal form (4NF) and fifth normal form (5NF). Higher normal forms eliminate more types of dependencies to optimize the database structure.
This document provides an overview of relational database management systems (RDBMS). It defines key database concepts like data, information, and database systems. It also explains the hierarchical structure of DBMS and compares flat file databases to relational databases. Relational databases incorporate multiple normalized tables that can be related to each other, while flat files put all data in a single table without relationships between files.
Integrity constraints are rules that help maintain data quality and consistency in a database. The main types of integrity constraints are:
1. Domain constraints specify valid values and data types for attributes to restrict what data can be entered.
2. Entity constraints require that each row have a unique identifier and prevent null values in primary keys.
3. Referential integrity constraints maintain relationships between tables by preventing actions that would invalidate links between foreign and primary keys.
4. Cascade rules extend referential integrity by automatically propagating updates or deletes from a primary table to its related tables.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
Normalization is the process of removing redundant data from your tables to improve storage efficiency, data integrity, and scalability.
Normalization generally involves splitting existing tables into multiple ones, which must be re-joined or linked each time a query is issued.
Why normalization?
The relation derived from the user view or data store will most likely be unnormalized.
The problem usually happens when an existing system uses unstructured file, e.g. in MS Excel.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
This document provides an overview of Boyce-Codd normal form (BCNF) which is a type of database normalization. It explains that BCNF was developed in 1974 and aims to eliminate redundant data and ensure data dependencies make logical sense. The document outlines the five normal forms including 1NF, 2NF, 3NF, BCNF, and 4NF. It provides examples of converting non-BCNF tables into BCNF by identifying and removing overlapping candidate keys and grouping remaining items into separate tables based on functional dependencies.
Functional dependencies (FDs) describe relationships between attributes in a database relation. FDs constrain the values that can appear across attributes for each tuple. They are used to define database normalization forms.
Some examples of FDs are: student ID determines student name and birthdate; sport name determines sport type; student ID and sport name determine hours practiced per week.
FDs can be trivial, non-trivial, multi-valued, or transitive. Armstrong's axioms provide rules for inferring new FDs. The closure of a set of attributes includes all attributes functionally determined by that set according to the FDs. Closures are used to identify keys, prime attributes, and equivalence of FDs.
This document discusses database normalization and different normal forms including 1NF, 2NF, 3NF, and BCNF. It defines anomalies like insertion, update, and deletion anomalies that can occur when data is not normalized. Examples are provided to illustrate the different normal forms and how denormalizing data can lead to anomalies. The key aspects of each normal form like removing repeating groups (1NF), removing functional dependencies on non-prime attributes (2NF), and removing transitive dependencies (3NF, BCNF) are explained.
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.
Dbms architecture
Three level architecture is also called ANSI/SPARC architecture or three schema architecture
This framework is used for describing the structure of specific database systems (small systems may not support all aspects of the architecture)
In this architecture the database schemas can be defined at three levels explained in next slide
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
The document discusses various SQL concepts like views, triggers, functions, indexes, joins, and stored procedures. Views are virtual tables created by joining real tables, and can be updated, modified or dropped. Triggers automatically run code when data is inserted, updated or deleted from a table. Functions allow reusable code and improve clarity. Indexes allow faster data retrieval. Joins combine data from different tables. Stored procedures preserve data integrity.
The document provides an overview of the relational model and relational algebra used in relational databases. It defines key concepts like relations, tuples, attributes, domains, schemas, instances, keys, and normal forms. It also explains the six basic relational algebra operations - select, project, union, difference, cartesian product, and rename - and how they can be composed to form complex queries. Examples of relations and queries involving operations like selection, projection, joins are provided to illustrate relational algebra.
4 the relational data model and relational database constraintsKumar
The document discusses the relational data model and constraints in relational databases. It begins by defining key concepts in the relational model such as relations, tuples, attributes, domains and relation schemas. It then covers relational constraints including key constraints, entity integrity constraints, and referential integrity constraints. Examples are provided to illustrate these concepts and constraints. The chapter aims to provide an overview of the formal relational model and constraints that must hold in relational databases.
Edgar Codd at IBM invented the relational database model in 1970 based on 13 rules. A relational database management system (RDBMS) stores data in related tables. RDBMSs help make data easy to store, retrieve, and combine in useful ways. Common RDBMSs include Microsoft SQL Server, Oracle, MySQL, and PostgreSQL. Tables are related through primary and foreign keys, which help enforce referential integrity.
CS3270 - DATABASE SYSTEM - Lecture (1)Dilawar Khan
This document outlines the key topics to be covered in a database course, including: understanding database concepts and the relational model, learning SQL for data manipulation and definition, database design techniques like entity-relationship modeling and normalization, and hands-on experience with Microsoft SQL Server. The course objectives are to help students understand databases and DBMS systems, apply relational concepts and SQL, and be able to design database applications. The document also provides an introduction to databases by comparing traditional file-based systems with the database approach.
This document provides an overview of various GUI tools that can be used for simple MySQL administration, including both MySQL's own tools like MySQL Administrator and MySQL Query Browser as well as third party tools like phpMyAdmin. It also lists some additional resources for MySQL Enterprise and training.
This document discusses various topics related to file and database management including:
- The definition of data processing and the two types of data access: sequential and direct.
- The four types of data management: data definition, data manipulation, queries/application generators, and report generators.
- Database management systems (DBMS) and examples of their applications.
- The three phases of database design: conceptual, logical, and physical.
Michael Joseph is giving a presentation on database normalization. He begins by explaining the importance of properly structuring data across database tables and the problems that can arise from poor database design, such as redundancy, inaccuracy, and consistency issues. He then describes database normalization as a process that organizes data to minimize redundancy by decomposing relations and isolating data in separate, well-defined tables connected through relationships. Different levels of normalization are discussed, with third normal form being sufficient for most applications. Examples are provided to illustrate how normalization progresses from first to third normal form. Potential issues with highly normalized databases are also outlined.
The document discusses database management systems (DBMS). It defines a database as a collection of related data and describes a DBMS as software that enables users to create, maintain and share databases. It provides an example of a university database with files for students, courses, grades and prerequisites. It outlines key characteristics of the database approach such as self-describing nature, insulation between programs and data, support of multiple views, and sharing of data.
The document introduces databases and database management systems (DBMS). It discusses that a DBMS is software that allows users to create, access, and manage data and databases. A DBMS is made up of four main components: users, a database, database applications, and the DBMS itself. The DBMS controls access to the database and enforces rules like security and data integrity. It also discusses some advantages of using a DBMS like improved data sharing and consistency.
The document discusses database design and relational database management systems. It covers key concepts like normalization, primary keys, foreign keys, and relationships between tables. Normalization is the process of organizing data to eliminate redundancy and ensure data is stored correctly. There are five normal forms with third normal form being sufficient for most applications. Tables are related through primary and foreign keys and different types of relationships can exist between tables like one-to-one, one-to-many, and many-to-many.
The document discusses various database models including flat file, hierarchical, network, relational, object-relational, and object-based models. It provides a brief history of database development, from manual files to relational databases. It describes key aspects of relational databases including how data is organized into logical tables with rows and columns.
The document discusses database concepts including:
- What a database is and its components like data, hardware, software, and users.
- Database management systems (DBMS) that enable users to define, create and maintain databases.
- Data models like hierarchical, network, and relational models. Relational databases using SQL are now most common.
- Database design including logical design, physical implementation, and application development.
- Key concepts like data abstraction, instances and schemas, normalization, and integrity rules.
Relational Database Management System part IIKavithaA19
This document discusses relational database management systems (RDBMS) and related concepts. It begins with definitions of RDBMS and relational database terminology like table, record, field, primary key, and foreign key. It then covers relational data structures using an example student table. Key aspects of relational data like tuples, relations, and attributes are defined. The document also discusses relational data integrity constraints and relational data manipulation languages. Finally, it provides an overview of Codd's rules for RDBMS.
This document provides an overview of databases and database management systems (DBMS). It discusses what a database is, components of a database system like users and applications, and examples of DBMS like MySQL and Oracle. It also summarizes key database concepts such as data models, relationships between data using keys, and relational algebra operations for querying databases.
The document discusses database system architecture and data models. It introduces the three schema architecture which separates the conceptual, logical and internal schemas. This provides logical data independence where the conceptual schema can change without affecting external schemas or applications. It also discusses various data models like hierarchical, network, relational and object-oriented models. Key aspects of each model like structure, relationships and operations are summarized.
The document discusses the key concepts of relational database management systems (RDBMS). It defines RDBMS and its core components like tables, records, fields, and keys. It also explains relational data structures and manipulation using SQL commands like INSERT, UPDATE, DELETE. Finally, it summarizes Codd's 12 rules for RDBMS which define the requirements for a database system to be considered truly relational.
This document introduces database management systems and relational database concepts. It defines key terms like data, data items, databases, DBMS, RDBMS, relations, tuples, attributes, domains and provides examples of common DBMS like MySQL, Oracle and DB2. It also summarizes different data models, database implementation levels, data independence, and relational model concepts such as primary keys, foreign keys and integrity constraints.
The document discusses relational databases and database management systems, including what a database is, the different levels of data abstraction (physical, logical, and view levels), purposes of databases like storage and security of data, database languages used to define, manipulate and control data, and relational database concepts such as tables, rows, columns, keys, and relational operations. It also covers database architecture, the relational model using tables, tuples, attributes, domains and keys, and relational algebra operations like selection, projection, joins, and set operations.
This document provides an overview of database management systems (DBMS). It defines a DBMS as a collection of data and applications used to access and manage that data. It briefly outlines the history of DBMS development from early hierarchical models to today's dominant relational model. It describes the purpose of a DBMS in ensuring data integrity, security, and consistency. It also covers data models, DBMS architecture including three levels (external, conceptual, internal), components, advantages and disadvantages. Finally, it discusses DBMS languages including data definition language to define schemas and data manipulation language to manage data.
This document provides an overview of database management systems (DBMS). It defines a DBMS as a collection of data and applications used to access and manage that data. The document then briefly discusses the history of DBMS development from early hierarchical models to today's dominant relational model. It describes the key purposes of using a DBMS, including reducing data redundancy and improving data integrity, security and consistency. The document outlines the main components and architecture of a DBMS, including its internal, conceptual and external levels. It also covers the advantages and disadvantages of using a DBMS, as well as common DBMS languages like SQL.
This document provides an introduction to relational databases and database management systems (DBMS). It discusses the purpose of databases and DBMS, including storing data efficiently and securely. It also describes key concepts like the three-schema architecture, database languages including SQL, database structures like tables and relations, and how programming languages can access SQL.
Data
Data is a collection of facts, such as numbers, words, measurements, observations or even just descriptions of things.
Data can be qualitative or quantitative.
Information
Information is data that has been processed in such a way as to be meaningful to the person who receives it.
it is any thing that is communicated.
This document defines basic database terminology and concepts. It describes key terms like database, tables, fields, records, cells, and objects. It also explains the differences between a database instance and schema. Additionally, it outlines the three schema architecture and how it provides data independence. Finally, it briefly discusses database system components, interfaces, utilities, and classification.
indian standard for Hard-Drawn Copper Conductors for Over Head Power Transmis...Chirag vasava
This document provides standard specifications for hard-drawn copper conductors used for overhead power transmission. It includes tables that specify the standard resistance, weight, diameter and other properties of both solid and stranded copper conductors of various sizes. Requirements cover the material properties of hard-drawn copper wire including resistivity, density, coefficients of expansion and temperature. Conductors must meet minimum standards for resistance, weight and diameter and be free of defects.
This document discusses methods for calculating conductor performance at high temperatures for the purpose of increasing the thermal rating of overhead transmission lines. It addresses the relationship between conductor temperature, sag, and tension, and identifies potential sources of error in heat balance and sag-tension calculations at high temperatures. These include errors related to varying wind conditions along the line, nonlinear conductor behavior, and assumptions about conductor properties. The document emphasizes that safety, structural integrity, and maintenance of electrical clearances must be considered when uprating transmission lines.
Hard-Drawn Copper Conductors for Over Head Power TransmissionChirag vasava
1. This document establishes standards and requirements for hard-drawn copper conductors used for overhead power transmission. It specifies dimensions, weights, resistances, and mechanical and electrical properties.
2. Standards include tables that specify the diameter, weight, and resistance for solid and stranded copper conductors. Tolerances on diameter and resistance are also provided.
3. The document describes test methods for mechanical properties like tensile strength and elongation, as well as electrical resistance tests to ensure conductors meet the specifications. It also outlines sampling procedures and criteria for acceptance or rejection of test results.
The Array is the most commonly used Data Structure.
An array is a collection of data elements that are of the same type (e.g., a collection of integers, collection of characters, collection of doubles).
OR
Array is a data structure that represents a collection of the same types of data.
The values held in an array are called array elements
An array stores multiple values of the same type – the element type
The element type can be a primitive type or an object reference
Therefore, we can create an array of integers, an array of characters, an array of String objects, an array of Coin objects, etc.
An ohmmeter is used to measure resistance and check continuity in electrical circuits. There are two main types - series and shunt. A series ohmmeter passes current through the internal battery and unknown resistor in series, while a shunt ohmmeter connects the meter movement and unknown resistor in parallel. Ohmmeters also differ in their display - analog ohmmeters show a continuous analog reading while digital ohmmeters provide an exact digital resistance value. Common applications include determining approximate resistances, sorting resistors, and checking diodes.
(S.C.E.T) Appliction of pll fm demodulation fsk demodulationChirag vasava
This document discusses applications of phase-locked loops (PLLs), including FM demodulation and FSK demodulation. It describes the internal block diagram of the LM565 PLL integrated circuit and introduces common PLL ICs. Applications of PLLs mentioned include frequency multiplication/division, frequency translation, AM detection, FM detection, and FSK demodulation. Circuit diagrams and operating principles are provided for FM demodulation and FSK demodulation using a PLL. Advantages of using a PLL for FM demodulation include high linearity and the ability to handle wider bandwidth signals.
its all about Artificial Intelligence(Ai) and Machine Learning and not on advanced level you can study before the exam or can check for some information on Ai for project
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
International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773
The growth of Internet and other web technologies requires the development of new
algorithms and architectures for parallel and distributed computing. International journal of
Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to
publish high quality scientific papers arising from original research and development from
the international community in the areas of parallel and distributed systems. IJDPS serves
as a platform for engineers and researchers to present new ideas and system technology,
with an interactive and friendly, but strongly professional atmosphere.
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
"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.
"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.
3. “A DBMS that manages data as collection of tables
in which all data relationships are represented by
common values in related tables.”
“A DBMS that follows all the twelve rules of CODD
is called RDBMS”
3
4. 4
Relational Database definition
All information must be represented explicitlyAll information must be represented explicitly
in one and only one way: as values in tables andin one and only one way: as values in tables and
each & every datum in the database must beeach & every datum in the database must be
accessible by specifying a table name, a columnaccessible by specifying a table name, a column
name, and a primary key.name, and a primary key.
5. 5
Data about various entities and their relationships
are stored in a series of logical tables (also known as
relations).
A relation is a two-dimensional table with certain imposed
restrictions:
1. Each Row is unique: No duplicate row
2. Entries in any column have the same domain.
3. Each column has a unique name
4. Order of the columns or rows is irrelevant
5. Each entry in the table is single valued:
No group item, repeating group, or array is allowed.
PRINCIPLES OF RELATIONAL MODELPRINCIPLES OF RELATIONAL MODEL
Note: A domain is the set of all possible values an attribute may assume.
Example: Domain of Major= (Acct, Mktg, Mgmt, ISOM, Fina)
6. Relational database: a set of relations.
Relation: made up of 2 parts:
– Schema : specifies name of relation, plus
name and type of each column.
• E.g. Students(sid: string, name: string, login: string,
age: integer, gpa: real)
– Instance : a table, with rows and columns.
•#rows = cardinality
•#fields = degree / arity
• Can think of a relation as a set of rows or tuples.
– i.e., all rows are distinct
6
7. The Relational Model developed by Dr. E. F. Codd at IBM
in the late 1960s
The model built on mathematical concepts, which
expounded in the famous work called "A Relational
Model of Data for Large Shared Databanks".
At the core of the relational model is the concept of a
table (also called a relation) in which all data is stored.
Records (horizontal rows also known as tuples) &
Fields (vertical columns also known as attributes).
It is important to note that how or where the tables of data
are stored makes no difference.
Table can be identified by a unique name.
This is quite a bit different from the Hierarchical & Network
models in which the user had to have an understanding
of how the data was structured within the
database in order to retrieve, insert, update, or
delete records from the database.
7
8. Advantages:
The data access methodology in relational
model is quite different from and better than the
earlier database models .
Another benefit of the relational system is that it
provides extremely useful tools for database
administration.
Meta-data (data about the table and field names
which form the database structure, access rights to
the database, integrity and data validation
rules etc).
Thus everything within the relational model can be
stored in tables. This means that many relational
systems can use operations recursively in order
to provide information about the database.
8
10. 10
Member of a relation type (set / table).Member of a relation type (set / table).
Tuples:Tuples:
Relation / Table Degree:Relation / Table Degree:
Attribute Value Set:Attribute Value Set:
Attribute Domain:Attribute Domain:
Attribute Name:Attribute Name:
Attribute (field):Attribute (field):
All attribute names must be unique within aAll attribute names must be unique within a
table / relation.table / relation.
A set of all possible values that can be attain byA set of all possible values that can be attain by
an attribute.an attribute.
Values currently contained in an attribute.Values currently contained in an attribute.
Number of attributes in a relation / table.Number of attributes in a relation / table.
Rows in a table / relation.Rows in a table / relation.
Cardinality:Cardinality: Number of tuples in a relation / table.Number of tuples in a relation / table.
11. 11
sid Name Login age GPA
53666 Jones Jones@ca 18 3.4
53444 smith Smith@ecs 18 3.2
53777 Blake Blake@aa 19 3.8
• Cardinality = 3, arity = 5 , all rows distinct
• Do all values in each column of a relation
instance have to be distinct?
Student(studno,name,address)
Course(courseno,lecturer)
Student(123,Bloggs,Woolton)
(321,Jones,Owens)
Schema
Instance
12. 12
Database SchemaDatabase Schema
The description of the database is calledThe description of the database is called
database schema. A database schema isdatabase schema. A database schema is
describe during database design and notdescribe during database design and not
expected to change frequently.expected to change frequently.
Schema DiagramSchema Diagram
Displayed schema is called schema diagram.Displayed schema is called schema diagram.
Each object in schema is called a schemaEach object in schema is called a schema
construct.construct.
13. 13
Database instance (occurrence or state)Database instance (occurrence or state)
The data in a database at a particularThe data in a database at a particular
moment of time.moment of time.
Intension & ExtensionIntension & Extension
The schema is sometimes called theThe schema is sometimes called the
intension and a database instance is calledintension and a database instance is called
an extension of the schema.an extension of the schema.
14. CD_ID Title Artist Genre
1 The Wall Pink Floyd Rock
2 Blue Train John Coltrane Jazz
3 Requiem W.A. Mozart Classical
14
Field
Record
Table
16. Tuple:
The actual data values for the attributes of a relation
are stored in tuples, or rows, of the table.
It is not necessary for a relation to have rows in order
to be a relation; even if no data exists for the relation
The relation remains defined with its set of attributes
Attribute:
The term attribute refers to characteristics.This
simply means that what the column contains will be
defined by the attribute of the column
16
19. Built in data integrity
Data consistency and accuracy
Easy data retrieval and data sharing
How and where the tables of data stored make no difference
You can access child table with out accessing parent table.
Non-navigational in nature
Find the data on the basis of the data values themselves.
One point data administration
Controlling redundancy
Data abstraction
Provide security
Data entry , update and deletion will be efficient.
Changes to the of the database is somewhat self-
documenting.
Support multiple users
19
20. 20
DBMS RDBMS
The concepts of relationships is
missing in a DBMS. If it exits it is
very less.
It is based on the concept
Of relationships
Speed of operation is very slow Speed of operation is very Fast
Hardware and Software requirements
are minimum
Hardware and Software
requirements are High
Platform used is normally DOS Platform used can be any DOS,
UNIX,VAX,VMS, etc
Uses concept of a file Uses concept of table
DBMS normally use 3GL RDBMS normally use a 4GL
Examples are dBase, FOXBASE, etc Examples are ORACLE, INGRESS,
SQL Server 2000 etc
22. 22
Country Capital
Italy Rome
India New Delhi
China Beijing
France Tokyo
Japan Paris
Country Currency
Italy Lira
India Rupee
China Quan
France Yen
Japan Francs
EXAMPES
23. 23
Left Door Right Door Hood Roof
Handle Window Lock
Engine Body Chassis
Car