The document provides an introduction to data structures using C. It discusses common data structures like arrays, linked lists, stacks, and queues. It then summarizes different ways to represent these structures in memory using arrays or pointers. Specifically, it discusses using pointers to link nodes together to represent linked lists, stacks and queues. It also discusses the distinction between the logical and physical representation of data structures.
The document provides an overview of the topics covered in a data processing course over 10 weeks. Week 1 covers data models and data types. Week 2 discusses data modelling and the components and importance of data models. Weeks 3 and 4 focus on database normalization, including 1NF, 2NF, 3NF and denormalization. Week 5 introduces the star schema model. Weeks 6-8 cover using Microsoft Access and relational data models. Week 9 reviews file organization techniques. Week 10 is for revision and exams.
This document provides an overview of designing databases. It begins with objectives like explaining relational databases and keys. It then discusses entity-relationship modeling including entities, attributes, relationships and E-R notation. Next, it covers converting E-R diagrams to tables by representing entity sets and relationship sets as tables. It also discusses concepts like strong/weak entities, total participation, and provides examples of E-R diagrams like for a banking system. The document is presented as part of a lecture on database design.
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.
- Database tables can be linked together through relationships that connect common fields, called primary keys, between tables. This allows data to be stored separately but managed and retrieved collectively.
- A relationship links data between individual tables and increases the usefulness of a database. A primary key uniquely identifies each record in a table and is used to link tables together through relationships.
- Junction tables are used to join primary keys from multiple tables and allow those tables to share information through a many-to-many relationship.
Relationships within the relational databaseJanecatalla
The document discusses different types of relationships in a relational database: one-to-one (1:1), one-to-many (1:M), and many-to-many (M:N). It states that the 1:M relationship is most common and should be the norm. While the 1:1 relationship can exist, it is rare. The M:N relationship cannot be directly implemented and instead requires creating a composite/linking entity to convert it to multiple 1:M relationships. Examples of each type of relationship are provided to illustrate how they are modeled and implemented in entity relationship diagrams and tables.
The document is a book about advanced Excel formulas and functions. It includes chapters on specific formulas to solve business problems, an introduction to array formulas, examples of commonly used formulas and functions, and additional resources. The author's goal is to help readers advance their Excel skills and learn to automate tasks using powerful formulas.
Data is a collection of distinct pieces of information that can be stored and processed digitally. A database is an organized collection of structured data stored digitally. A database management system (DBMS) is software that allows users to define, create, maintain and control access to a database. Common DBMSs include Oracle, SQL Server, and MySQL. Relational databases organize data into tables with rows and columns and allow users to define relationships between tables. Keys like primary keys and foreign keys help define these relationships and uniquely identify rows. Structured Query Language (SQL) is used to communicate with databases to perform operations like querying and updating data.
Entity Integrity Constraint:
It states that in a relation no attribute of a primary key (K) can have a null value. If a K consists of a single attribute, this constraint obviously applies on this attribute, so it cannot have the Null value. However, if a K consists of multiple attributes, then none of the attributes of this K can have the Null value in any of the instances.
Referential Integrity Constraint :
This constraint is applied to foreign keys. Foreign key is an attribute or attribute combination of a relation that is the primary key of another relation. This constraint states that if a foreign key exists in a relation, either the foreign key value must match the primary key value of some tuple in its home relation or the foreign key value must be completely null.
The document provides an overview of the topics covered in a data processing course over 10 weeks. Week 1 covers data models and data types. Week 2 discusses data modelling and the components and importance of data models. Weeks 3 and 4 focus on database normalization, including 1NF, 2NF, 3NF and denormalization. Week 5 introduces the star schema model. Weeks 6-8 cover using Microsoft Access and relational data models. Week 9 reviews file organization techniques. Week 10 is for revision and exams.
This document provides an overview of designing databases. It begins with objectives like explaining relational databases and keys. It then discusses entity-relationship modeling including entities, attributes, relationships and E-R notation. Next, it covers converting E-R diagrams to tables by representing entity sets and relationship sets as tables. It also discusses concepts like strong/weak entities, total participation, and provides examples of E-R diagrams like for a banking system. The document is presented as part of a lecture on database design.
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.
- Database tables can be linked together through relationships that connect common fields, called primary keys, between tables. This allows data to be stored separately but managed and retrieved collectively.
- A relationship links data between individual tables and increases the usefulness of a database. A primary key uniquely identifies each record in a table and is used to link tables together through relationships.
- Junction tables are used to join primary keys from multiple tables and allow those tables to share information through a many-to-many relationship.
Relationships within the relational databaseJanecatalla
The document discusses different types of relationships in a relational database: one-to-one (1:1), one-to-many (1:M), and many-to-many (M:N). It states that the 1:M relationship is most common and should be the norm. While the 1:1 relationship can exist, it is rare. The M:N relationship cannot be directly implemented and instead requires creating a composite/linking entity to convert it to multiple 1:M relationships. Examples of each type of relationship are provided to illustrate how they are modeled and implemented in entity relationship diagrams and tables.
The document is a book about advanced Excel formulas and functions. It includes chapters on specific formulas to solve business problems, an introduction to array formulas, examples of commonly used formulas and functions, and additional resources. The author's goal is to help readers advance their Excel skills and learn to automate tasks using powerful formulas.
Data is a collection of distinct pieces of information that can be stored and processed digitally. A database is an organized collection of structured data stored digitally. A database management system (DBMS) is software that allows users to define, create, maintain and control access to a database. Common DBMSs include Oracle, SQL Server, and MySQL. Relational databases organize data into tables with rows and columns and allow users to define relationships between tables. Keys like primary keys and foreign keys help define these relationships and uniquely identify rows. Structured Query Language (SQL) is used to communicate with databases to perform operations like querying and updating data.
Entity Integrity Constraint:
It states that in a relation no attribute of a primary key (K) can have a null value. If a K consists of a single attribute, this constraint obviously applies on this attribute, so it cannot have the Null value. However, if a K consists of multiple attributes, then none of the attributes of this K can have the Null value in any of the instances.
Referential Integrity Constraint :
This constraint is applied to foreign keys. Foreign key is an attribute or attribute combination of a relation that is the primary key of another relation. This constraint states that if a foreign key exists in a relation, either the foreign key value must match the primary key value of some tuple in its home relation or the foreign key value must be completely null.
The document discusses key concepts related to databases and database management systems. It defines what a database is as a collection of organized data and what a database management system (DBMS) is as software that enables users to create, access, manage and control a database. It provides examples of common DBMS like MySQL and Microsoft Access. The document also summarizes the purpose of databases in reducing data redundancy and inconsistencies while facilitating data sharing and security. Finally, it discusses important database concepts like data models, normalization, and relational model terminology.
DEE 431 Database keys and Normalisation Slide 2YOGESH SINGH
This document provides information about a database programming course, including topics covered, data models, database keys, and normalization. The topics covered are data models, database keys, and normalization. It defines different data models including conceptual, physical, and logical models. It explains different database keys like primary keys, foreign keys, composite keys, and surrogate keys. It also discusses the concepts of normalization including various normal forms from 1NF to 3NF and the advantages of normalization in reducing data redundancy and improving data integrity.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
It is a semantic data model that is used for the graphical representation of the conceptual database design. The semantic data models provide more constructs that is why a database design in a semantic data model can contain/represent more details. With a semantic data model, it becomes easier to design the database, at the first place, and secondly it is easier to understand later. We also know that conceptual database is our first comprehensive design. It is independent of any particular implementation of the database, that is, the conceptual database design expressed in E-R data model can be implemented using any DBMS. For that we will have to transform the conceptual database design from E-R data model to the data model of the particular DBMS. There is no DBMS based on the E-R data model, so we have to transform the conceptual database design anyway.
This document summarizes three papers on keyword search over structured databases using an interpretative approach. The first paper discusses building an efficient index table to map keywords to row and column identifiers in the database. The second paper presents a general algorithm with two steps - a publication step to pre-compute indexing, and a search step to lookup keywords and generate SQL queries. The third paper introduces the concept of intrinsic and contextual weights to model the dependency between query keywords and generate a ranked list of query interpretations.
The document discusses key concepts in relational database management systems including:
1) Everything is represented as relations (tables) with attributes (columns) and tuples (rows) that make up the relations.
2) Schemas define the structure of relations with attributes and primary keys to uniquely identify tuples.
3) Relations can be related through foreign keys that match primary keys in other relations.
4) Integrity rules like entity and referential integrity enforce valid relationships between tuples in different relations.
This document provides definitions and explanations of key concepts in database management systems. It defines DBMS, RDBMS, SQL, databases, tables, fields, primary keys, unique keys, foreign keys, joins, normalization, denormalization, indexes, views, stored procedures, triggers, and more. It also explains differences between concepts like DELETE vs TRUNCATE and local vs global variables.
This document discusses the SQL language for relational databases. It covers the background and history of SQL, the SQL standards, and the key statements and features of SQL including data definition, data types, schema and table creation, attributes, constraints, keys and referential integrity. The document provides examples of SQL statements and clauses to define schemas, tables, attributes, primary keys, foreign keys and other constraints.
An extended database reverse engineering – a key for database forensic invest...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Physical and logical data independence are described. Logical data independence refers to the ability to change the logical schema without impacting external schemas or application programs. Physical data independence is the ability to change the physical schema without affecting the logical schema, such as changing storage structures or devices. Examples of each type of independence are provided. The key elements of an entity-relationship model - entities, attributes, and relationships - are defined. An example E-R diagram showing students, courses, and their relationship is depicted to illustrate these elements.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
IRJET- Resume Information Extraction FrameworkIRJET Journal
The document discusses a framework for extracting information from resumes. Resumes are semi-structured documents that contain varying information like different fields, field names, and formats, making them difficult to parse. The proposed framework uses text mining and rule-based parsing to extract keywords from resumes, scores qualifications and skills, clusters the extracted information using DBSCAN, and classifies the resumes using gradient boosting machines. It aims to help recruiters filter and categorize large numbers of resumes more efficiently.
Warp Films is an independent British film company that struggles to compete with large conglomerate studios like Disney due to a massive budget gap. For example, the budget for Disney's Pirates of the Caribbean was $300 million, compared to just £1.5 million for Warp Film's 2007 movie This is England. New technologies like HD filming and digital distribution have helped Warp promote and distribute films at a lower cost. However, conglomerates have more resources to combat piracy and reach a much broader audience with big-budget blockbusters aimed at all ages.
El documento describe los pasos para construir una pista de carreras de dos círculos concéntricos. Primero se delineó el contorno de la pista usando una circunferencia mayor, luego se agregó una circunferencia menor compartiendo el mismo centro, formando la figura geométrica de dos círculos concéntricos.
This document discusses the conventions of a school-based drama film genre. It outlines the traditional main characters, which include the protagonist rookie who is unpopular at first, the attractive "jock", and the antagonist who is the most popular and dislikes the rookie. It describes the typical narrative of the rookie joining the school without friends, falling for the jock, having issues arise due to the antagonist, and ultimately ending with the rookie and jock together while the antagonist is dismissed. Examples of films that follow these conventions are provided. The document also briefly describes the openings of two example school drama films, Lean on Me and Dangerous Minds, noting elements of mise-en-scene. In conclusion, the author decides to
This document is a quiz about the United Kingdom, testing knowledge of basic facts like the country represented by the Scottish flag, the British national anthem, the Queen of England, and the capital of Wales. Participants are asked to select the correct answers by clicking on buttons or numbers.
The document discusses the social production of data through online activities. It notes that internet companies like Facebook and Google store vast amounts of user data, including deleted posts and viewing histories. It also discusses how this data can be used to analyze social and commercial trends. Researchers are working on tools to reconstruct digital histories and give users more control over their data. The amount of personal data being stored continues to grow dramatically.
The document discusses 10 open questions about conserving electronic artworks. Some of the key questions addressed include how to preserve ephemeral net art for future generations; whether the entire artwork or just source files/code should be preserved; and whether individual artworks or entire ecosystems/networks should be the focus of conservation efforts. The questions touch on issues like technological obsolescence, dependence on specific platforms/networks, and how the nature of conservation may need to change over time.
The document discusses key concepts related to databases and database management systems. It defines what a database is as a collection of organized data and what a database management system (DBMS) is as software that enables users to create, access, manage and control a database. It provides examples of common DBMS like MySQL and Microsoft Access. The document also summarizes the purpose of databases in reducing data redundancy and inconsistencies while facilitating data sharing and security. Finally, it discusses important database concepts like data models, normalization, and relational model terminology.
DEE 431 Database keys and Normalisation Slide 2YOGESH SINGH
This document provides information about a database programming course, including topics covered, data models, database keys, and normalization. The topics covered are data models, database keys, and normalization. It defines different data models including conceptual, physical, and logical models. It explains different database keys like primary keys, foreign keys, composite keys, and surrogate keys. It also discusses the concepts of normalization including various normal forms from 1NF to 3NF and the advantages of normalization in reducing data redundancy and improving data integrity.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
It is a semantic data model that is used for the graphical representation of the conceptual database design. The semantic data models provide more constructs that is why a database design in a semantic data model can contain/represent more details. With a semantic data model, it becomes easier to design the database, at the first place, and secondly it is easier to understand later. We also know that conceptual database is our first comprehensive design. It is independent of any particular implementation of the database, that is, the conceptual database design expressed in E-R data model can be implemented using any DBMS. For that we will have to transform the conceptual database design from E-R data model to the data model of the particular DBMS. There is no DBMS based on the E-R data model, so we have to transform the conceptual database design anyway.
This document summarizes three papers on keyword search over structured databases using an interpretative approach. The first paper discusses building an efficient index table to map keywords to row and column identifiers in the database. The second paper presents a general algorithm with two steps - a publication step to pre-compute indexing, and a search step to lookup keywords and generate SQL queries. The third paper introduces the concept of intrinsic and contextual weights to model the dependency between query keywords and generate a ranked list of query interpretations.
The document discusses key concepts in relational database management systems including:
1) Everything is represented as relations (tables) with attributes (columns) and tuples (rows) that make up the relations.
2) Schemas define the structure of relations with attributes and primary keys to uniquely identify tuples.
3) Relations can be related through foreign keys that match primary keys in other relations.
4) Integrity rules like entity and referential integrity enforce valid relationships between tuples in different relations.
This document provides definitions and explanations of key concepts in database management systems. It defines DBMS, RDBMS, SQL, databases, tables, fields, primary keys, unique keys, foreign keys, joins, normalization, denormalization, indexes, views, stored procedures, triggers, and more. It also explains differences between concepts like DELETE vs TRUNCATE and local vs global variables.
This document discusses the SQL language for relational databases. It covers the background and history of SQL, the SQL standards, and the key statements and features of SQL including data definition, data types, schema and table creation, attributes, constraints, keys and referential integrity. The document provides examples of SQL statements and clauses to define schemas, tables, attributes, primary keys, foreign keys and other constraints.
An extended database reverse engineering – a key for database forensic invest...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Physical and logical data independence are described. Logical data independence refers to the ability to change the logical schema without impacting external schemas or application programs. Physical data independence is the ability to change the physical schema without affecting the logical schema, such as changing storage structures or devices. Examples of each type of independence are provided. The key elements of an entity-relationship model - entities, attributes, and relationships - are defined. An example E-R diagram showing students, courses, and their relationship is depicted to illustrate these elements.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
IRJET- Resume Information Extraction FrameworkIRJET Journal
The document discusses a framework for extracting information from resumes. Resumes are semi-structured documents that contain varying information like different fields, field names, and formats, making them difficult to parse. The proposed framework uses text mining and rule-based parsing to extract keywords from resumes, scores qualifications and skills, clusters the extracted information using DBSCAN, and classifies the resumes using gradient boosting machines. It aims to help recruiters filter and categorize large numbers of resumes more efficiently.
Warp Films is an independent British film company that struggles to compete with large conglomerate studios like Disney due to a massive budget gap. For example, the budget for Disney's Pirates of the Caribbean was $300 million, compared to just £1.5 million for Warp Film's 2007 movie This is England. New technologies like HD filming and digital distribution have helped Warp promote and distribute films at a lower cost. However, conglomerates have more resources to combat piracy and reach a much broader audience with big-budget blockbusters aimed at all ages.
El documento describe los pasos para construir una pista de carreras de dos círculos concéntricos. Primero se delineó el contorno de la pista usando una circunferencia mayor, luego se agregó una circunferencia menor compartiendo el mismo centro, formando la figura geométrica de dos círculos concéntricos.
This document discusses the conventions of a school-based drama film genre. It outlines the traditional main characters, which include the protagonist rookie who is unpopular at first, the attractive "jock", and the antagonist who is the most popular and dislikes the rookie. It describes the typical narrative of the rookie joining the school without friends, falling for the jock, having issues arise due to the antagonist, and ultimately ending with the rookie and jock together while the antagonist is dismissed. Examples of films that follow these conventions are provided. The document also briefly describes the openings of two example school drama films, Lean on Me and Dangerous Minds, noting elements of mise-en-scene. In conclusion, the author decides to
This document is a quiz about the United Kingdom, testing knowledge of basic facts like the country represented by the Scottish flag, the British national anthem, the Queen of England, and the capital of Wales. Participants are asked to select the correct answers by clicking on buttons or numbers.
The document discusses the social production of data through online activities. It notes that internet companies like Facebook and Google store vast amounts of user data, including deleted posts and viewing histories. It also discusses how this data can be used to analyze social and commercial trends. Researchers are working on tools to reconstruct digital histories and give users more control over their data. The amount of personal data being stored continues to grow dramatically.
The document discusses 10 open questions about conserving electronic artworks. Some of the key questions addressed include how to preserve ephemeral net art for future generations; whether the entire artwork or just source files/code should be preserved; and whether individual artworks or entire ecosystems/networks should be the focus of conservation efforts. The questions touch on issues like technological obsolescence, dependence on specific platforms/networks, and how the nature of conservation may need to change over time.
Este documento describe un curso de inglés de 16 semanas con 2 créditos y 2 unidades de aprendizaje que se ofrece a través del Instituto Virtual de Lenguas. El curso se basa en el aprendizaje por tareas y contiene entornos de información inicial, conocimiento, aprendizaje colaborativo, evaluación, seguimiento del aprendizaje y gestión del estudiante.
Kajian ini bertujuan untuk mengenal pasti sebab-sebab pelajar kelas akhir tidak membawa peralatan seni visual semasa kelas dan mencadangkan langkah untuk meningkatkan minat pelajar terhadap subjek tersebut."
Planning is the most important aspect of database design. Before using tools, planners should verify requirements, diagram data, and plan the database design on paper. This results in databases that meet requirements and expectations. When designing, planners aim to minimize data redundancy without losing information. They also seek to avoid anomalies like insertion, deletion, and update anomalies. Relational databases can provide flexibility over single-table designs by reducing redundancy and anomalies through normalization.
This document discusses topics related to data structures and algorithms. It covers structured programming and its advantages and disadvantages. It then introduces common data structures like stacks, queues, trees, and graphs. It discusses algorithm time and space complexity analysis and different types of algorithms. Sorting algorithms and their analysis are also introduced. Key concepts covered include linear and non-linear data structures, static and dynamic memory allocation, Big O notation for analyzing algorithms, and common sorting algorithms.
- Normalization is the process of structuring a database to minimize duplication and define relationships clearly. There are five normal forms that provide guidelines for optimal database design.
- Normalization specifies design criteria to guide the database structure and identify problems. It provides rules to reorganize data in a consistent, clean fashion.
- Denormalization intentionally introduces some data duplication to improve performance of complex queries by reducing the number of table joins required. It is typically done on read-only systems like data warehouses.
The document discusses different types of databases including relational databases, analytical databases, operational databases, and object-oriented databases. It describes key characteristics of each type of database such as how they model and store data. Relational databases use tables to store data and link tables using relationships while analytical databases store archived data for analysis and operational databases manage dynamic data. Object-oriented databases integrate object-oriented programming with databases.
Introduction to database with ms access(DBMS)07HetviBhagat
A database is an organized collection of structured data stored electronically in a computer system. The document discusses database components including hardware, software, data, procedures, and access languages. It provides examples of database systems like MS Access and how it can be used to create tables, enter and query data, and perform other operations. Key database terms are defined such as entities, attributes, relationships, and database administrators' roles and responsibilities. Advantages and disadvantages of database management systems are also outlined.
Introduction to database with ms access.hetvii07HetviBhagat
A database is usually controlled by a database management system (DBMS). MS Access is a popular DBMS that allows users to create and manage databases. The document discusses various components of a database such as tables, queries, forms and reports. It provides information on how to create an MS Access database, add tables, enter data, create relationships between tables, write queries to extract data, and build forms and reports. The key aspects covered are data modeling using entity relationship diagrams, normalizing data to reduce redundancy, and performing common database operations like importing, exporting and analyzing data in MS Access.
Database management system by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
A database is an organized collection of structured data stored electronically in a computer system. A database management system (DBMS) is a complex software system used to create and manage databases and properly maintain large and complex databases. A DBMS provides logical and physical views of the data and allows for different external views for different users. It also provides languages to define, manipulate and control access to the data.
This reading introduces you to data modeling and different types of data models. Data models help keep data consistent and enable people to map out how data is organized. A basic understanding makes it easier for analysts and other stakeholders to make sense of their data and use it in the right ways.
Important note: As a junior data analyst, you won't be asked to design a data model. But you might come across existing data models your organization already has in place.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
3. Physical data modeling depicts how a database operates. A physical data model defines all entities and attributes used; for example, it includes table names, column names, and data types for the database.
The document provides an introduction to database management systems (DBMS). It defines key concepts such as data, records, tables, relations, and databases. It explains that a DBMS consists of the database itself and a management system. The management system establishes rules and procedures for creating, organizing, and manipulating the data in the database. The document also discusses three schemas - the physical schema (how data is stored), logical schema (database structure), and external schema (views for end users). It introduces the concepts of data independence, which aims to minimize changes needed across schemas if changes are made.
1) Organizations now deal with huge amounts of data both internally and externally generated to better understand their business and customers.
2) Relational databases cannot effectively handle this big data due to challenges in data structure, scaling, and speed.
3) NoSQL databases provide alternatives to store structured, semi-structured, and unstructured data across different data models like columnar, key-value, document, and graph. Each type has different properties suited for various use cases.
A Relational Database Management System (RDBMS) is a server that manages data for you. The data is structured into tables, where each table has some number of columns, each of which has a name and a type. For example, to keep track of James Bond movies, we might have a “movies” table that records the title (a string), year of release (a number), and the actor who played Bond in each movie (an index into a table of Bond actors).
This document provides an introduction to SQL Server for beginners. It discusses prerequisites for learning SQL such as knowledge of discrete mathematics. It explains that SQL Server runs as a service and can be accessed via tools like SQL Server Management Studio. The document also covers basic concepts in SQL Server including how data is stored and organized in tables, columns, rows and databases. It defines primary keys and discusses different data types. Finally, it discusses the client-server model and how SQL Server can be accessed from client applications via libraries, web services, and other connectivity options.
The document discusses plans for a database project for Kudler Fine Foods to support an online ordering system, noting that it will allow customers to place orders online and have them ready for pickup, as well as expanding their customer base. It analyzes the necessary database tables, including tables for products, customers, orders, and additional tables needed like one to reserve inventory and track shipping methods. The goal of the online ordering system is to boost profits by increasing the customer base and making the ordering process more convenient.
The document defines key database concepts such as data, information, databases, data modeling, and database management systems (DBMS). It describes what a database is, the basic database structure, and the process of data modeling. It also discusses different types of DBMS software, database designs, and types of databases including relational, distributed, cloud, NoSQL, object-oriented, and graph databases. Additionally, it covers data manipulation using SQL and database advantages like redundancy control and disadvantages like costs.
The document provides an overview and syllabus for a course on fundamentals of data structures. It covers topics such as linear and non-linear data structures including arrays, stacks, queues, linked lists, trees and graphs. It describes various data types in C like integers, floating-point numbers, characters and enumerated types. It also discusses operations on different data structures and analyzing algorithm complexity.
The document provides an overview of the syllabus for a Data Structures course. It discusses topics that will be covered including arrays, linked lists, stacks, queues, trees, and graphs. It also outlines the course grading breakdown and covers basic terminology related to data structures such as data, data items, records, and files. Common data structure operations like traversing, searching, inserting, and deleting are also defined. Lastly, it provides guidance on selecting appropriate data structures based on the problem constraints and required operations.
The document discusses key concepts in data modeling including:
1) The importance of data modeling in creating a logical database that reduces redundancy and enables efficient data retrieval.
2) How business rules are translated into data model components such as entities, attributes, and relationships.
3) The emergence of standard languages like DML and DLL which helped standardize early network data models.
The document compares conceptual, logical, and physical data models. Conceptual models show entities and relationships without attributes or keys. Logical models add attributes, primary keys, and foreign keys. Physical models specify tables, columns, data types, and foreign keys to represent the database implementation. The complexity increases from conceptual to logical to physical models.
1. Introduction to Data Structures Using C
A data structure is an arrangement of data in a computer's memory or even disk storage. An
example of several common data structures are arrays, linked lists, queues, stacks, binary
trees, and hash tables.
Algorithms, on the other hand, are used to manipulate the data contained in these data
structures as in searching and sorting
Name Position
Aaron Manager
Charles VP
George Employee
Jack Employee
Janet VP
John President
Kim Manager
Larry Manager
Martha Employee
Patricia Employee
Rick Secretary
Sarah VP
Susan Manager
Thomas Employee
Zack Employee
But only one view of the company is shown by the above list. You also want the database to
represent the relationships between employees and management at ABC. It does not tell you
which managers are responsible for which workers and so on, although your list contains both
name and position. After thinking about the problem for a while. You decide that the tree
diagram is much better structure for showing the work relationships at the ABC company.
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2. These two above diagrams are examples of the different data structures. Your data is
organized into a list, in one of the data structures which is above. The employees name can be
stored in alphabetical order so that we can locate the employee's record very quickly. For
showing the relationships between employees these structure is not very useful. The tree
structure is a much better suited for this purpose.
The data structures are an important way of organizing information in a computer. There are
many different data structures that programmers use to organize data in computers, just like
the above illustrated diagrams. Some of the data structures are similar to the tree diagram
because they are good for representing the relationships between different data. Other
structures are good for ordering the data in a particular way like the list of employees which is
shown above. Each data structure has their own unique properties that make it well suited to
give a certain view of the data.
Pointers and Indirection
The problem with representing Data Structures Using C that are not linear. We need some way
to map these data structures to the computer's linear memory. One solution to this, is to use
pointers.
Pointers are the memory locations that are stored in the memory cells. By using a pointer, by
holding a memory address rather than the data one memory cell can point to another memory
cell.
The memory cell at address 2003 contains a pointer the address of another cell,
which can be seen in the above figure. Here the pointer is pointing to the memory
cell 2005 which contains the letter C. This means that, we have two ways of
accessing the letter C. We can refer to the memory cell which contains the value C
directly or we can use the pointer to refer to it indirectly. The process of accessing
the data through pointers is known as indirection. Using pointers, we can also create
multiple levels of indirection.
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3. Linear Data Structures
Pointers can become very complex and difficult to use by having many levels of indirection.
When used pointers incorrectly, it can make data structures very difficult to understand. The
tradeoff between complexity and flexibility should be consider whenever you use pointers in
constructing data structures.
The idea of pointers and indirection is not exclusive to the computer memory. Pointers appear
in many different aspects of computer's usage. Hyperlinks in web pages is a good example
pointers. This links are really the pointers to the other web page. Perhaps you have even
experienced the double indirection when you went to visit the familiar web site and found the
site had moved. You saw a notice that the web pages had been moved and a link to the new
site, instead of the page you expected.
We saw that it is very simple to create data structures that are organized similar to the way
the computer's memory is organized. For example, the list of employee's from the ABC
company is a linear data structure.
Since the computer's memory is also linear, it is very easy to see how we can represent this
list with the computer's memory. Any data structure which organizes the data elements one
after the other is known as linear data structure. So far we have seen two examples of linear
data structures: the string data structure and the ABC company list.
You may have noticed that these two examples of linear data structures resemble to each
other. This is because they both are really different kinds of lists. In general, all linear data
structures look like the list. However, this does not mean that all the linear data structures are
exactly the same. Suppose I want to design a list to store the names of the ABC employees in
the computer. One possible design is to organize the names similar to the example picture
above. Another possible design is to use the pointers we learned about in the last lesson.
While these two designs provide the same functionality the way they are implemented in the
computer is much different. This means that there is an abstract view of a list which is distinct
from any particular computer implementation
You may have also noticed that the picture of the ABC employees is not as exactly the same
as the original list. When we make a list of names, we tend to organize this list into a column
rather than a row. In this case, the conceptual or logical representation of a list is the column
of names. However, the physical representation of the list in the computer's memory is the
row of strings. For most data structures, the way that we think about them is far different
from the way they are implemented in the computer. In other words, the physical
representation is much different from that of the logical representation, especially in data
structures that use pointers.
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4. Ordered List: The Abstract View
The most common linear data structure used is the list. By now you are already pretty familiar
with the idea of a list and at least one way of representing a list in the computer. Now we are
going to look at a particular kind of list: an ordered list. Ordered lists are very similar to the
alphabetical list of employee names for the ABC company. These lists keep items in a specific
order such as alphabetical or numerical order. Whenever an item is added to the list, it is
placed in the correct sorted position so that the entire list is always sorted.
Before we consider how to implement such a list, we need to consider the abstract view of an
ordered list. Since the idea of an abstract view of a list may be a little confusing, let's think
about a more familiar example. Consider the abstract view of a television. Regardless of who
makes a television, we all expect certain basic things like the ability to change channels and
adjust the volume. As long as these operations are available and the TV displays the shows we
want to view, we really don't care about who made the TV or how they chose to construct it.
The circuitry inside the TV set may be very different from one brand to the next, but the
functionality remains the same. Similarly, when we consider the abstract view of an ordered
list, we don't worry about the details of implementation. We are only concerned with what the
list does, not how it does it.
Suppose we want a list that can hold the following group of sorted numbers: [2 4 6 7]. What
are some things that we might want to do with our list? Well, since our list is in order, we will
need some way of adding numbers to the list in the proper place, and we will need some way
of deleting numbers we don't want from the list. To represent these operations, we will use
the following notation:
AddListItem(List, Item)
RemoveListItem(List, Item)
Each operation has the name and the list of parameters the operation needs. The parameter
list for the AddListItem operation includes a list and an item. The RemoveListItem operation is
very similar except this time we will specify the item we want to remove. These operations are
part of the abstract view of an ordered list. They are what we expect from any ordered list
regardless of how it is implemented in the computer's memory.
Array Implementation
One approach to creating a list is simply to reserve a block of adjacent memory cells to large
enough to hold the entire list. Such a block of memory is called as array. Of course, since we
want to add items to our list, we need to reserve more than just four memory cells. For now,
we will make our array large enough to hold as much as six numbers.
Pointer Implementation
A second approach to creating a list is to link groups of memory cells together using the
pointers. Each group of memory cells is called as a node. With this implementation every node
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5. contains the data item and the pointer to the next item in the list. You can picture this
structure as a chain of nodes linked together by the pointers. As long as we know where the
chain begins, we can follow the links to reach any item in the list. Often this structure is called
as a linked list.
Notice that the last memory cell in our chain contains the symbol called "Null". This symbol is
a special value that tells us that we have reached the end of our list. You can think that this
symbol as a pointer that points to nothing. Since we are using the pointers to implement our
list, the list operations AddListItem and the RemoveListItem will work differently than they did
for sequential lists.
Linear Data Structures
Stack is the other common linear data structure which is been used now a days. Just like we
did with the ordered list, we will examine the abstract view of the stack first and then look at
the couple of ways a stack can be implemented.
Stack is very similar to a list except that a stack is more restricted. The figure below should
give you an good idea of the abstract view of what stack is. Follow the directions to
manipulate the simple stack and learn about the operations that the stack provides.
By seing the figure above you can see that this data structure is really a restricted list. You
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6. have restricted the access to one end of the list by using the pop and push operations. The
result of this restriction is that items in the list will be stored one on top of the other. We must
first remove all the items above it till you get to the bottom item. "Last-In, First-Out" or LIFO,
which is used to describe the behavior, since the last item to enter the stack is the first item to
leave the stack. The top item is the item always the last item to enter the stack and it is
always the first item to leave the stack since no other items can be removed until the top item
is removed.
PushStackItem(Stack, Item)
Item PopStackItem(Stack)
The PushStackItem operation has two parameters which are, the stack and an item. This
operation adds the item to the top of the specified stack, the item and the stack which is
specified in the above function. The PopStackItem operation only takes one parameter which
is a stack(stack name). However, notice that this operation has the keyword Item listed to the
left of the PopStackItem keyword. This keyword is used to represents the item that is removed
from the top of the stack when the PopStackItem operation is done. These two
operations(PushStackItem and PopStackItem) are part of the abstract view of the stack.
Pointer Implementation in the Stack
In order to implement a stack using pointers, we need to link nodes together just like we did
for the pointer implementation of the list. Each node contains the stack item and the pointer to
the next node. We also need a special pointer(stack pointer) to keep track of the top of our
stack.
Stack Operations:
push(new-item:item-type)
Adds an item onto the stack.
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7. top():item-type
Returns the last item pushed onto the stack.
pop()
Removes the most-recently-pushed item from the stack.
is-empty():Boolean
True iff no more items can be popped and there is no top item.
is-full():Boolean
True iff no more items can be pushed.
get-size():Integer
Returns the number of elements on the stack.
All operations except get-size() can be performed in O(1) time. get-size() runs in at
worst O(N).
Queues
The Queue is the final linear data structure that we will examined. Like the stack, the queue is
also a type of restricted list. Instead of restricting all the operations to only one end of the list
as a stack does, the queue allows items to be added at the one end of the list and removed at
the other end of the list. The figure below should give you a good idea of the abstract view of
the queue.
This restrictions placed on a queue cause the structure to be a "First-In, First-Out" or FIFO
structure. This idea is similar to customer lines at a in any bill payment store(eg. phone bill
payment queue). When customer A is ready to check out, he or she enters the tail(end) of the
waiting line. When the preceding customers have paid, then customer A pays and exits from
the head of the line. The bill-payment line is really a queue that enforces a "first come, first
serve" policy.
We will represent these two operations with the following notation:
EnqueueItem(Queue, Item)
Item DequeueItem(Queue)
These two operations are very similar to that of the operations we learned for the stack data
structure. Although the names are different, the logic of using the parameters is the same.
The EnqueueItem(enter the queue item) operation takes the Item parameter and adds it to
the tail(end) of Queue. The DequeueItem(delete queue item) operation removes the head
item of Queue and returns this as Item. Notice that we represent the returned item with a
keyword located to the left of the operation name. These two operations are part of the
abstract view of a queue. Regardless of how we choose to implement our queue on the
computer, the queue must support these two operations.
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8. The Implementation View
When we looked at the ordered list and stack data structures, we saw two different ways to
implement each one of them. Although the implementations were different, the data structure
was still the same from the abstract point of view for both stack and ordered list. We could still
use the same operations on the data structures regardless of their implementations. With the
queue, it is also possible to have various implementations that support the operations
EnqueueItem and DequeueItem. The distinction between the logical representation of the
queue and the physical representation of the queue. Remember that the logical representation
is the way that we think of the way data being stored in the computer. The physical
representation is the way the way data is actually organized in the memory cells(computer
memory).
Now let's consider how the EnqueueItem and DequeueItem operations might be implemented
in the queue. To store letters into the queue, we could advance the tail pointer by one location
and add the new letter in the queue. To dequeue(delete) letters, we could remove out the
head letter and increase the head pointer by one location. While this approach seems very
straight forward, it has a serious problem. As items are added and removed, our queue will
march straight through the entire memory of the computer. We have not limited the size of
our queue to a fixed amount of size.
Perhaps we could limit the size of the queue by not allowing the tail pointer to advance beyond
the certain memory location. This implementation would stop the queue from traversing the
entire memory, but it would only allow us to fill the queue one time. Once the head and tail
pointers reached the stop location, our queue would no longer work untill we delete some data
from it.
Trees: The Abstract View
Another common nonlinear data structure is the tree.
In this diagram above, we can see that the starting point, or the root node, is circled in blue
colour. A node is a simple structure that holds data and links to other nodes.
In this case, our root node contains the data string "John" and three links to the other nodes.
Notice that the group of nodes circled in red do not have any links(childs). These nodes are at
the end of the branches and they are appropriately called as leaves or leaf nodes. In our
diagram, the nodes are interconnected with solid black lines called arcs or edges. These edges
show the relationships between the nodes in the tree.
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9. One important relationship in the binary tree is the parent-child relationship. Parent nodes
have at least one edge to the node lower in the tree. This lower node is called the child node.
Nodes can have more than one child, but the children can only have a single parent. Notice
that the root node has no parent, and the leaf nodes has no children. Final feature to note in
our diagram is the subtree. At each level of the tree, we can see that the tree structure is
repeated. For example, the two nodes representing "Charles" and "Rick" compose a very
simple tree with "Charles" as the root node and and "Rick" as a single leaf node.
Trees are implemented in the computer's memory. We will begin by introducing the simple
tree structure called the binary tree. Binary trees have the restriction that nodes can't have
more than two children. With this restriction, we can easily determine how to represent a
single binary node in the memory. Our node will need to reserve memory for the data and two
pointers(for pointing two childs of that node).
Structure of a binary node:
Using our binary nodes, we can construct a binary tree. In the data cell of each node, we will
can store a letter. The physical representation of our tree might look something like the figure
below:
Introduction to Graphs
The last data structure that we will study in this tutorial is the graphs. Graphs are similar to
trees except that, they do not have as many restrictions.
In the previous tutorial, we saw that every tree has a root node, and all the other nodes in the
tree are children of a perticular node. We also saw that the nodes can have many children but
VIJAYA COLLEGE Page 9
10. only one parent. When we relax these restrictions, we get the graph data structure. The logical
representation of a typical graph might look something like the figure shown below:
It is not hard to imagine how the graph data structure could be useful for representing the
data. Perhaps each of the nodes above could represent a city and the edges connecting the
nodes could represent the roads. Or we could use a graph to represent a computer network
where the nodes are workstations and the edges are the network connections. Graphs have so
many applications in the computer science and mathematics that several algorithms have
been written to perform the standard graph operations such as searching the graph and
finding the shortest path between nodes of a graph.
Notice that our graph does not have any root node like the tree data structure. Instead, any
node can be connected with any other node in the graph. Nodes do not have any clear parent-
child relationship like we saw in the tree. Instead nodes are called as neighbors if they are
connected by an edge. For example, node A above has three neighbors: B, C, and D.
Now that you have a basic idea of the logical representation of the graphs, let's take a look at
one way that graphs are commonly represented in computers. The representation is called an
adjacency matrix, and it uses the two-dimensional array to store the information about the
graph nodes. The adjacency matrix for our graph is given below.
A B C D E F
A -- 1 1 1 -- --
B 1 -- 1 -- 1 --
C 1 1 -- -- -- --
D 1 -- -- -- 1 1
E -- 1 -- 1 -- --
F -- -- -- 1 -- --
Notice that the matrix shown above has six rows and six columns labeled with the nodes from
the graph. We mark a '1' in a cell if there exists an edge from two nodes that index that cell.
For example, since we have a edge between A and B, we mark a '1' in the cells indexed by A
VIJAYA COLLEGE Page 10
11. and B. These cells are marked with a dark gray background in the adjacency matrix. With our
adjacency matrix, we can represent every possible edge that our graph can have.
Quiz for Data Structures Using C
1. _____________ Memory is Volatile
main
Random Access
Both 1 and 2
Virtual
2. An _________ data type is a keyword of a programming language that
specifies the amount of memory needed to store data and the kind of data
that will be stored in that memory location
abstract
int
vector
None of these
3. Which of the following abstract data types are NOT used by Integer
Abstract Data type group?
Short
Int
float
long
4. The hashString() member function is called by other member functions of
the Hashtable class whenever a function needs to convert a
________________
a hash number key to a key
key to a hash number key
VIJAYA COLLEGE Page 11
12. a key to an Index
None of these
5. An application iterates the hashtable by calling the ______ and ______
member functions
hasNext() and hasDelete()
hasNext() and getNextKey()
Both 1 and 2
None of these
6. The java.util package contains two classes that are designed to work with
hashtables. They are _______ and _________..
Hashtable , HashMap class
Hashtable,List
Vector,List
Vector,Hashtable
7. Data members of the Hashtable class stored in the private access
specifier
private access specifier
Public access specifier
common access specifier
None of these
8. _____ is the common programming technique used for hashing in all
hashing functions
Cloning
Bit Shifting
Hashmapping
Listing
VIJAYA COLLEGE Page 12
13. 9. If the depth of a tree is 3 levels, then what is the Size of the Tree?
4
2
8
6
10. deleteNode() function requires the _____ of the data element of the
node that is being removed
reference
value
declaration
variable
11. Value of the first linked list index is _______
One
Zero
-1
None of these
12. A linked list index is ____ that represents the position of a node in a
linked list.
An Integer
a variable
a character
a boolean
13. Why is the constructor of the QueueLinkedList class empty?
because initialization of data members of the LinkedList class is performed by the
constructor of the LinkedList class.
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14. because initialization of data members of the LinkedList class is performed by the
destructor of the LinkedList class.
because initialization of data members of the QueueLinkedList class is performed by the
constructor of the LinkedList class.
because initialization of data members of the QueueLinkedList class is performed by the
destructor of the LinkedList class
14. _______ form of access is used to add and remove nodes from a queue
LIFO,Last In First Out
FIFO , First In First Out
Both 1 and 2
None of these
15. ______ form of access is used to add and remove nodes from a stack
LIFO
FIFO
Both 1 and 2
None of these
16. New nodes are added to the _____ of the queue.
front
back
middle
Both 1 and 2
17. A _______ is a data structure that organizes data similar to a line in the
supermarket, where the first one in line is the first one out.
queue linked list
stacks linked list
both of them
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15. neither of them
18. In an array queue, data is stored in an _____ element.
Node
linked list
array
constructor
19. The pop() member function determines if the stack is empty by calling
the _____ member function
removeback()
isEmpty()
removedfront()
hasNext()
20. What happens when you push a new node onto a stack?
the new node is placed at the front of the linked list.
the new node is placed at the back of the linked list.
the new node is placed at the middle of the linked list.
No Changes happens
// Implementation Of BINARY TREE OPERATION (insertion & Deletion)
#include<conio.h>
#include<stdio.h>
#include<malloc.h>
#include<process.h>
struct node
{
struct node *llink;
struct node *rlink;
int data;
};
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16. void main()
{
struct node *head,*t;
int s,d;
struct node* finsert();
struct node* delenode(struct node*,int);
void insert(struct node *);
void inorder(struct node *);
clrscr();
head=NULL;
do
{
printf("
1-Insertion");
printf("
2-Deletion");
printf("
3-Inorder");
printf("
4-Exit");
printf("
Enter Choice:");
scanf("%d",&s);
switch(s)
{
case 1://insertion
if(head==NULL)
{
head=finsert();
}
else
insert(head);
break;
case 2://Deletion
if(head==NULL)
printf("
Binary Tree Empty.......");
else
{
printf("
Enter Data to delete:");
scanf("%d",&d);
if(head->llink==NULL && head->rlink==NULL && head->data==d)
{
t=head;
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