Introduction to Relational algebra in DBMS - The relational algebra is explained with all the operations. Some of the examples from the textbook is also solved and explained.
This document discusses SQL commands for creating tables, adding data, and enforcing integrity constraints. It covers the core SQL commands: DDL for defining schema, DML for manipulating data, DCL for controlling access, DQL for querying data, and TCL for transactions. Specific topics summarized include data types, primary keys, foreign keys, indexes, views, stored procedures, functions and triggers. Integrity constraints like NOT NULL, UNIQUE, CHECK, DEFAULT are explained. The document also covers SQL queries with filtering, sorting, patterns and ranges. Authorization using GRANT and REVOKE commands is briefly covered.
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.
Database normalization is the process of refining the data in accordance with a series of normal forms. This is done to reduce data redundancy and improve data integrity. This process divides large tables into small tables and links them using relationships.
Here is the link of full article: https://www.support.dbagenesis.com/post/database-normalization
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.
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.
A SQL subquery is a query nested inside another query. Subqueries can be used in the SELECT, WHERE, INSERT, UPDATE, or DELETE clauses. The subquery executes first and its results are then used by the outer query. There are three types of subqueries: single row, multiple row, and multiple column. Single row subqueries use comparison operators like =, <, > and return one row. Multiple row subqueries use operators like IN, ANY, ALL and return multiple rows. Multiple column subqueries compare more than one column between the outer and inner queries.
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.
This document discusses entity-relationship (E-R) modeling concepts including specialization, generalization, aggregation, and redundant relationships. It provides definitions and examples of each concept. Specialization defines subclasses based on distinguishing attributes. Generalization defines a superclass from common attributes of subclasses. Aggregation treats relationships as higher-level entities. An E-R diagram example shows representing aggregation. Another example shows a diagram with redundant relationships.
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
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.
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.
The document summarizes a lecture on relational algebra and calculus. It defines relational algebra as a theoretical language used to query databases using operations that work on relations. It describes the five fundamental relational algebra operations and additional join, division, and aggregate operations. It then defines relational calculus as specifying what to retrieve from a database rather than how, and describes tuple and domain relational calculus using predicates, quantifiers, and tuple variables.
This slide explains the conversion procedure from ER Diagram to Relational Schema.
1. Entity set to Relation
2. Relationship set to Relation
3. Attributes to Columns, Primary key, Foreign Keys
Integrity constraints are rules used to maintain data quality and ensure accuracy in a relational database. The main types of integrity constraints are domain constraints, which define valid value sets for attributes; NOT NULL constraints, which enforce non-null values; UNIQUE constraints, which require unique values; and CHECK constraints, which specify value ranges. Referential integrity links data between tables through foreign keys, preventing orphaned records. Integrity constraints are enforced by the database to guard against accidental data damage.
This document discusses database normalization forms and dependencies. It covers:
- The two levels of discussing relation schema quality (logical and implementation)
- Informal measures of quality like semantics, redundancy, NULL values, and spurious tuples
- Functional dependencies, inference rules, closure, and finding a minimal cover
- First, second, third, and BCNF normal forms and their definitions/conditions
- Non-prime and prime attributes
- Other dependencies like multivalued, join, and their relationships to higher normal forms.
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.
Relational Algebra is a procedural query language consisting of a set of operations that take one or two relations as input and produce a new relation as output. The fundamental operations in Relational Algebra are selection, projection, union, set difference, cartesian product, and join. Selection chooses tuples that meet a selection condition, projection chooses attributes from a relation, union includes all tuples from two relations, set difference includes tuples from one relation not in another, cartesian product creates all combinations of tuples from two relations, and join compounds similar tuples from two relations.
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.
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.
The document outlines the steps for mapping an ER or EER model to a relational database schema. It discusses:
1. The 7 steps for mapping entity types, relationship types, attributes, and other constructs from an ER model to relations. This includes mapping entities, relationships, attributes, specializations/generalizations.
2. Additional steps 8 and 9 for mapping special constructs from an EER model like specialization/generalization and categories/union types. Various options for mapping these constructs are presented.
3. Examples are provided throughout to illustrate how each modeling construct in sample ER/EER diagrams would be mapped to relations and keys following the outlined steps. Figures show both the ER/EER
An array is a data structure that stores fixed number of items of the same type. It allows fast access of elements using indices. Basic array operations include traversing elements, inserting/deleting elements, searching for elements, and updating elements. Arrays are zero-indexed and elements are accessed via their index.
This document discusses ADO.NET, which is a set of classes that allows .NET applications to communicate with databases. It provides advantages over classic ADO such as supporting both connected and disconnected data access. The key components of ADO.NET are data providers, which act as bridges between applications and databases, and the DataSet, which allows storing and manipulating relational data in memory disconnected from the database.
This document provides an overview of working with databases and MySQL. It discusses database concepts like tables, records, fields, primary keys, and relationships. It also covers MySQL topics such as creating and selecting databases, defining tables, adding/retrieving/updating/deleting records, and modifying user privileges. The goal is to teach the basics of working with databases and the MySQL database management system.
The document discusses relational algebra, which is a formal language used to query and manipulate relations in a relational database. It describes the basic operations in relational algebra like selection, projection, join, union, set difference, etc. and provides examples of how to write queries using each of these operations.
The document discusses the relational model for databases. The relational model represents data as mathematical n-ary relations and uses relational algebra or relational calculus to perform operations. Relational calculus comes in two flavors: tuple relational calculus (TRC) and domain relational calculus (DRC). TRC uses tuple variables while DRC uses domain element variables. Expressions in relational calculus are called formulas and queries return tuples that make the formula evaluate to true.
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.
This document discusses entity-relationship (E-R) modeling concepts including specialization, generalization, aggregation, and redundant relationships. It provides definitions and examples of each concept. Specialization defines subclasses based on distinguishing attributes. Generalization defines a superclass from common attributes of subclasses. Aggregation treats relationships as higher-level entities. An E-R diagram example shows representing aggregation. Another example shows a diagram with redundant relationships.
This document provides an overview of entity-relationship modeling as a first step for designing a relational database. It describes how to model entities, attributes, relationships, and participation constraints. Key aspects covered include using boxes to represent entity types, diamonds for relationship types, and labeling relationships with degrees. The document also discusses handling multi-valued attributes and deciding whether to model concepts as attributes or entity types.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
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.
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.
The document summarizes a lecture on relational algebra and calculus. It defines relational algebra as a theoretical language used to query databases using operations that work on relations. It describes the five fundamental relational algebra operations and additional join, division, and aggregate operations. It then defines relational calculus as specifying what to retrieve from a database rather than how, and describes tuple and domain relational calculus using predicates, quantifiers, and tuple variables.
This slide explains the conversion procedure from ER Diagram to Relational Schema.
1. Entity set to Relation
2. Relationship set to Relation
3. Attributes to Columns, Primary key, Foreign Keys
Integrity constraints are rules used to maintain data quality and ensure accuracy in a relational database. The main types of integrity constraints are domain constraints, which define valid value sets for attributes; NOT NULL constraints, which enforce non-null values; UNIQUE constraints, which require unique values; and CHECK constraints, which specify value ranges. Referential integrity links data between tables through foreign keys, preventing orphaned records. Integrity constraints are enforced by the database to guard against accidental data damage.
This document discusses database normalization forms and dependencies. It covers:
- The two levels of discussing relation schema quality (logical and implementation)
- Informal measures of quality like semantics, redundancy, NULL values, and spurious tuples
- Functional dependencies, inference rules, closure, and finding a minimal cover
- First, second, third, and BCNF normal forms and their definitions/conditions
- Non-prime and prime attributes
- Other dependencies like multivalued, join, and their relationships to higher normal forms.
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.
Relational Algebra is a procedural query language consisting of a set of operations that take one or two relations as input and produce a new relation as output. The fundamental operations in Relational Algebra are selection, projection, union, set difference, cartesian product, and join. Selection chooses tuples that meet a selection condition, projection chooses attributes from a relation, union includes all tuples from two relations, set difference includes tuples from one relation not in another, cartesian product creates all combinations of tuples from two relations, and join compounds similar tuples from two relations.
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.
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.
The document outlines the steps for mapping an ER or EER model to a relational database schema. It discusses:
1. The 7 steps for mapping entity types, relationship types, attributes, and other constructs from an ER model to relations. This includes mapping entities, relationships, attributes, specializations/generalizations.
2. Additional steps 8 and 9 for mapping special constructs from an EER model like specialization/generalization and categories/union types. Various options for mapping these constructs are presented.
3. Examples are provided throughout to illustrate how each modeling construct in sample ER/EER diagrams would be mapped to relations and keys following the outlined steps. Figures show both the ER/EER
An array is a data structure that stores fixed number of items of the same type. It allows fast access of elements using indices. Basic array operations include traversing elements, inserting/deleting elements, searching for elements, and updating elements. Arrays are zero-indexed and elements are accessed via their index.
This document discusses ADO.NET, which is a set of classes that allows .NET applications to communicate with databases. It provides advantages over classic ADO such as supporting both connected and disconnected data access. The key components of ADO.NET are data providers, which act as bridges between applications and databases, and the DataSet, which allows storing and manipulating relational data in memory disconnected from the database.
This document provides an overview of working with databases and MySQL. It discusses database concepts like tables, records, fields, primary keys, and relationships. It also covers MySQL topics such as creating and selecting databases, defining tables, adding/retrieving/updating/deleting records, and modifying user privileges. The goal is to teach the basics of working with databases and the MySQL database management system.
The document discusses relational algebra, which is a formal language used to query and manipulate relations in a relational database. It describes the basic operations in relational algebra like selection, projection, join, union, set difference, etc. and provides examples of how to write queries using each of these operations.
The document discusses the relational model for databases. The relational model represents data as mathematical n-ary relations and uses relational algebra or relational calculus to perform operations. Relational calculus comes in two flavors: tuple relational calculus (TRC) and domain relational calculus (DRC). TRC uses tuple variables while DRC uses domain element variables. Expressions in relational calculus are called formulas and queries return tuples that make the formula evaluate to true.
Relational algebra is the formal system used to manipulate data in a relational database. It consists of operators like select, project, join, union, and difference that are applied to relations to retrieve or modify data. Relational calculus provides an alternative, non-procedural way to query databases using logic and variables. Both are based on relational and algebraic concepts and underpin the functionality of relational databases and SQL.
The document discusses relational algebra, which is a mathematical system used to represent queries on relational databases. It describes the core relational algebra operators like selection, projection, join, and set operations. It also introduces additional operators like renaming and theta joins. Expression trees are presented as a way to visually represent complex sequences of relational algebra operations used to query multiple relations.
Lecture 06 relational algebra and calculusemailharmeet
The document discusses data manipulation languages (DML) for databases. There are two main types of DML: navigational/procedural and non-navigational/non-procedural. Relational algebra is a non-navigational DML defined by Codd that uses algebraic operations like selection, projection, join, etc. on tables. Relational calculus is also a non-navigational DML that defines new relations in terms of predicates on tuple variables ranging over named relations.
Relational algebra is a collection of operations to manipulate or access tables. It includes set-oriented operations like selection, projection, join, division, as well as native relational operations like rename and assignment. Additional relational operators like aggregate functions, grouping, and outer joins were added to enhance its expressive power.
This document introduces relational algebra, which is an algebra for modeling and querying data stored in relational databases. Relational algebra uses operators like projection, selection, join, union, difference and rename to filter, combine and transform relations (tables) of data. Several examples are provided to demonstrate how these operators can be combined to solve increasingly complex queries against sample databases. The document concludes by noting that while relational algebra defines the underlying logic, most databases are instead queried using SQL, which is based on this relational algebra.
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The document describes relational algebra and calculus operations for querying relational databases. It outlines unary operations like select, project, and rename that operate on a single relation as well as binary operations derived from set theory like union, intersection, and difference that combine two relations. Examples are provided to illustrate how sequences of relational algebra operations can be used to formulate queries and retrieve data from the COMPANY example database.
The document discusses the relational data model structure and operations. It describes the key concepts of the relational data model including relations, attributes, tuples/rows, domains, schemas, keys such as candidate keys and foreign keys. It also explains the basic relational algebra operations like selection, projection, union, set difference, intersection and cartesian product along with examples.
This document discusses C++ streams and stream classes. It explains that streams represent the flow of data in C++ programs and are controlled using classes. The key classes are istream for input, ostream for output, and fstream for file input/output. It provides examples of reading from and writing to files using fstream, and describes various stream manipulators like endl. The document also discusses the filebuf and streambuf base classes that perform low-level input/output operations.
Triggers are stored procedures that are automatically executed in response to data modification events like insert, update or delete on a table. There are two types of triggers - DML triggers which respond to data manipulation statements, and DDL triggers which respond to data definition language statements like create, alter or drop. Triggers can be used to perform validation, auditing or other actions in response to data changes. They can be disabled, enabled or modified as needed using SQL statements.
The document discusses embedded SQL statements and stored procedures in Oracle databases. Embedded SQL allows SQL statements to be placed within a host programming language like C/C++. Stored procedures are named PL/SQL blocks that perform specific database-related tasks. Triggers are PL/SQL blocks that automatically execute in response to data changes, such as inserts or updates.
Este documento describe los desencadenadores en SQL Server, incluidos los desencadenadores DML y DDL. Los desencadenadores DML se activan cuando se ejecutan sentencias DML como INSERT, UPDATE y DELETE, mientras que los desencadenadores DDL se activan por eventos como la creación o eliminación de tablas. El documento también explica cómo crear, modificar y eliminar desencadenadores.
This document discusses files and streams in C++. It explains that the fstream library allows reading from and writing to files using ifstream, ofstream, and fstream objects. It covers opening, closing, writing to, and reading from files, noting that files must be opened before use and should be closed after. The standard openmode arguments and open(), close(), write, and read syntax are provided. Examples of reading from and writing to files are included.
This document discusses SQL views, including:
- Views are virtual tables derived from other tables that do not store data themselves. Views allow presenting data from multiple tables as a single table.
- Advantages of views include security, convenience, simplicity, and integrity. Disadvantages can include performance issues and restrictions on updating views.
- The CREATE VIEW statement is used to define views. Various types of views - horizontal, vertical, subset, and grouped - are described.
- Conditions for updating views are outlined. The CHECK OPTION can be used to restrict updates and inserts to views to only rows that satisfy the view definition.
- CASCADED and LOCAL options determine how CHECK OPTION conditions are applied
The document discusses database recovery techniques. It describes the purpose of database recovery as bringing the database to its last consistent state prior to a failure. It discusses different types of failures and data update techniques like deferred and immediate updating. For deferred updating, recovery involves redoing transactions after the last checkpoint, while for immediate updating, recovery uses undo for active transactions and redo for committed transactions.
The document discusses the organization and planning of laboratory services in a hospital. It covers key aspects like types of clinical and pathology labs, changing role of labs, importance of labs, principles of planning, infrastructure planning considering size, location, functional areas, quality control and quality assurance. The goal is to provide accurate and timely diagnostic support through well-planned lab services.
The document discusses database recovery techniques, including:
- Recovery algorithms ensure transaction atomicity and durability despite failures by undoing uncommitted transactions and ensuring committed transactions survive failures.
- Main recovery techniques are log-based using write-ahead logging (WAL) and shadow paging. WAL protocol requires log records be forced to disk before related data updates.
- Recovery restores the database to the most recent consistent state before failure. This may involve restoring from a backup and reapplying log entries, or undoing and reapplying operations to restore consistency.
Relational algebra is the basic set of operations for the relational model, including unary operations like selection and projection, binary operations like various join types, and set operations like union and intersection. Relational algebra operations manipulate relations and produce new relations, allowing users to specify database queries. Common operations include selection to filter tuples, projection to select attributes, equijoins to match tuples on equality conditions, and outer joins to retain non-matching tuples.
Five basic operations in relational algebra: Selection, Projection, Cartesian product, Union, and Set Difference.
These perform most of the data retrieval operations needed.
Also have Join, Intersection, and Division operations, which can be expressed in terms of 5 basic operations
The document discusses relational algebra and operations that can be performed on relations in a relational database. It describes unary operations like select and project that operate on a single relation, as well as binary operations like join, union, and difference that combine two relations. It also covers additional operations like aggregation, outer joins, and recursive closure that provide more advanced querying capabilities beyond the core relational algebra. The goal of relational algebra is to allow users to use a sequence of algebraic operations to specify database queries and retrieve relation data in a declarative manner.
The document discusses various concepts in relational databases including:
- Relation schemas define the structure of relations with attributes.
- Relations are sets of tuples that conform to a relation schema.
- Keys such as candidate keys and primary keys uniquely identify tuples.
- Foreign keys in one relation refer to primary keys in another.
- Relational algebra operators manipulate relations, including select, project, join, union and more.
This document discusses query languages and relational algebra operations. It introduces relational algebra as a procedural query language. The basic relational algebra operations are selection, projection, union, set difference, cartesian product, and rename. Examples are provided to illustrate each operation. Additional operations like join, outer join, division and aggregation are also discussed. The document concludes with a discussion of database modification operations like deletion, insertion and updating.
The document discusses various concepts related to relational databases including:
- Primary keys uniquely identify rows in a table. Foreign keys match values in other tables' primary keys.
- Relational databases represent data using relations which have a schema and instances consisting of tuples.
- Relational algebra operations like selection, projection, join, etc. allow querying relational data.
What is Relational model
Characteristics
Relational constraints
Representation of schemas
characteristics and Constraints of Relational model with proper examples.
Updates and dealing with constraint violations in Relational model
Relational algebra is a set of operations used to specify queries on relational databases. The basic operations include select, project, join, union, intersection, and cartesian product. A sequence of relational algebra operations forms a query that manipulates relations and returns a relation of results. Common uses of the operations include retrieving subsets of tuples, projecting columns, joining tables, and set operations to combine relations.
relational model in Database Management.ppt.pptRoshni814224
This document provides an overview of the relational model used in database management systems. It discusses key concepts such as:
- Relations, which are sets of tuples that represent entities and relationships between entities.
- Relation schemas that define the structure of relations, including the attributes and their domains.
- Keys such as candidate keys and foreign keys that uniquely identify tuples and define relationships between relations.
- Relational algebra, which consists of operators like select, project, join, and set operations to manipulate and query relations.
- An example banking schema is presented to demonstrate these concepts.
3._Relational_Algebra.pptx:Basics of relation algebraZakriyaMalik2
Relational algebra is a set of operations used to specify queries in relational databases. There are unary operations like selection and projection that operate on a single relation, and binary operations like join, union, and difference that combine two relations. Aggregate functions can also be applied to compute values like count, sum, average, minimum and maximum over groups of tuples. A sequence of relational algebra operations forms an expression that represents the query.
Dbms ii mca-ch5-ch6-relational algebra-2013Prosanta Ghosh
The document discusses relational algebra, which defines a set of operations for the relational model. The relational algebra operations can be divided into two groups: set operations from mathematical set theory including UNION, INTERSECTION, and SET DIFFERENCE; and operations developed specifically for relational databases including SELECT, PROJECT, and JOIN. The six basic relational algebra operators are SELECT, PROJECT, UNION, INTERSECTION, SET DIFFERENCE, and CARTESIAN PRODUCT. RELATIONAL expressions allow sequences of these operations to be combined to retrieve and manipulate data from relations.
Unit-II DBMS presentation for students.pdfajajkhan16
Relational databases organize data into one or more tables made up of rows and columns to show relationships between different data structures. Relationships are logical connections between tables established based on interactions among the tables. Relational algebra provides theoretical foundations for relational databases and SQL through operators like select, project, join, and union that take relations as input and output new relations. Relational calculus is a non-procedural query language that uses predicates and quantifiers to specify what to retrieve from relations rather than how to retrieve it.
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.
This document provides an overview of data modeling and SQL. It introduces key concepts in relational databases including relations, schemas, tuples, domains, keys, and referential integrity. It also describes the relational data model including the structure of relations, attributes, and relation instances. Finally, it covers the relational algebra including operations like select, project, join, union, difference, and rename that form the basis for SQL queries. The document uses examples from a banking domain to illustrate these concepts.
The document provides an overview of relational algebra and database concepts including:
- The basic structure of relations and relation schemas using an example of customer data.
- Key concepts like primary keys, foreign keys, and relationships between relations.
- The six basic relational algebra operations - select, project, union, set difference, cartesian product, and rename. Examples are given for each.
- Additional relational algebra operations like set intersection, natural join, division, and assignment are described along with banking examples.
- The document concludes with a mention of extended relational algebra operations like generalized projection, aggregate functions, and outer join.
This document provides an overview of a lecture on mobile communications. It covers the history of mobile communication technologies, applications of mobile devices, challenges of wireless transmission, and media access protocols. It also discusses wireless telecommunication systems including GSM, UMTS, satellite systems, wireless LANs, and support for mobility in applications and transport protocols. The outlook section discusses potential future directions in mobile communication research and development.
The document discusses rules and algorithms for converting between postfix notation and expression trees. It provides rules for building an expression tree from postfix notation by appending nodes based on the order in the postfix list. It also provides rules for generating prefix notation by traversing the expression tree from left to right and outputting the value of each node visited. Examples are given to demonstrate converting a postfix expression to an expression tree and then to prefix notation step-by-step.
This document discusses and provides code examples for three sorting algorithms: insertion sort, selection sort, and quicksort. It includes pseudocode for insertion sort and describes its worst case performance on sample data. Selection sort code is shown along with an example of its worst case. Quicksort is briefly explained. Merge sort is also listed along with sample data being sorted.
This document provides guidance on preparing for and participating in a job interview. It discusses researching the company and position, anticipating questions, practicing delivery, and maintaining a positive demeanor. The interview process is broken down into an introductory, assessment, and concluding period. Candidates are advised to demonstrate enthusiasm, honesty, active listening, and analytical skills while avoiding negativity. Common interview questions are also listed.
This document discusses artificial intelligence and its various branches. It defines AI as computers with the ability to mimic human intelligence through functions like learning from experience, solving problems with missing information, and understanding language and images. The major branches of AI discussed are perceptive systems, robotics, expert systems, learning systems, natural language processing, neural networks, and vision systems. Intelligent behaviors like learning, problem solving, and reacting to new situations are also outlined.
A computer is an electronic device that processes data according to instructions. Early computers included the abacus, Pascal's adding machine, and Babbage's analytical engine. The first modern computers used vacuum tubes, then transistors replaced tubes. Integrated circuits placed many transistors on chips, driving down costs and leading to personal computers in the 1970s and the Internet in the 1980s and 1990s.
Multi-currency in odoo accounting and Update exchange rates automatically in ...Celine George
Most business transactions use the currencies of several countries for financial operations. For global transactions, multi-currency management is essential for enabling international trade.
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George
Analytic accounts are used to track and manage financial transactions related to specific projects, departments, or business units. They provide detailed insights into costs and revenues at a granular level, independent of the main accounting system. This helps to better understand profitability, performance, and resource allocation, making it easier to make informed financial decisions and strategic planning.
How to Set warnings for invoicing specific customers in odooCeline George
Odoo 16 offers a powerful platform for managing sales documents and invoicing efficiently. One of its standout features is the ability to set warnings and block messages for specific customers during the invoicing process.
K12 Tableau Tuesday - Algebra Equity and Access in Atlanta Public Schoolsdogden2
Algebra 1 is often described as a “gateway” class, a pivotal moment that can shape the rest of a student’s K–12 education. Early access is key: successfully completing Algebra 1 in middle school allows students to complete advanced math and science coursework in high school, which research shows lead to higher wages and lower rates of unemployment in adulthood.
Learn how The Atlanta Public Schools is using their data to create a more equitable enrollment in middle school Algebra classes.
INTRO TO STATISTICS
INTRO TO SPSS INTERFACE
CLEANING MULTIPLE CHOICE RESPONSE DATA WITH EXCEL
ANALYZING MULTIPLE CHOICE RESPONSE DATA
INTERPRETATION
Q & A SESSION
PRACTICAL HANDS-ON ACTIVITY
A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
How to Customize Your Financial Reports & Tax Reports With Odoo 17 AccountingCeline George
The Accounting module in Odoo 17 is a complete tool designed to manage all financial aspects of a business. Odoo offers a comprehensive set of tools for generating financial and tax reports, which are crucial for managing a company's finances and ensuring compliance with tax regulations.
GDGLSPGCOER - Git and GitHub Workshop.pptxazeenhodekar
This presentation covers the fundamentals of Git and version control in a practical, beginner-friendly way. Learn key commands, the Git data model, commit workflows, and how to collaborate effectively using Git — all explained with visuals, examples, and relatable humor.
Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
2. Relational Algebra
Relational algebra and relational calculus are
formal languages associated with the relational
model.
The Relational Algebra is used to define the ways
in which relations (tables) can be operated to
manipulate their data.
It is used as the basis of SQL for relational
databases, and illustrates the basic operations
required of any DML.
2
3. 3
Relational Algebra
Relational algebra operations work on one or
more relations to define another relation
without changing the original relations.
Both operands and results are relations, so
output from one operation can become input to
another operation.
Allows expressions to be nested, just as in
arithmetic. This property is called closure.
4. 4
Relational algebra VS Relational Calculus
Informally, relational algebra is a (high-level)
procedural language and relational calculus a
non-procedural language. – Difference ??
However, formally both are equivalent to one
another.
A language that produces a relation that can be
derived using relational calculus is relationally
complete.
What & How
5. 5
Relational Algebra
This Algebra is composed of Unary operations
(involving a single table) and Binary operations
(involving multiple tables).
Five basic operations in relational algebra:
Selection, Projection, Cartesian product,
Union, and Set Difference.
These perform most of the data retrieval
operations needed.
Also have Join, Intersection, and Division
operations, which can be expressed in terms of
5 basic operations.
8. 8
Selection (or Restriction)
σpredicate (R)
– Works on a single relation R and defines a relation that
contains only those tuples (rows) of R that satisfy the
specified condition (predicate).
– Unary Operation
σ <condition> < tablename >
Conditions in Selection:
Simple Condition: (attribute)(comparison)(attribute)
(attribute)(comparison)(constant)
Comparison: =,≠,≤,≥,<,>
9. Select Operator Example
Name Age Weight
Harry 34 80
Sally 28 64
George 29 70
Helena 54 54
Peter 34 80
Name Age Weight
Harry 34 80
Helena 54 54
Peter 34 80
Person бAge≥34(Person)
Name Age Weight
Helena 54 54
бAge=Weight(Person)
10. 10
Example - Selection (or Restriction)
List all staff with a salary greater than £10,000.
σsalary >10000 (Staff)
11. 11
Projection
Πcol1, .. . ,coln(R)
– Works on a single relation R and defines a
relation that contains a vertical subset of R,
extracting the values of specified attributes and
eliminating duplicates.
π <columnlist > < tablename >
e.g., name of employees:
∏ name(Employee)
e.g., name of employees earning more than 80,000:
∏ name(бSalary>80,000(Employee))
12. Project Operator Example
Name Age Salary
Harry 34 80,000
Sally 28 90,000
George 29 70,000
Helena 54 54,280
Peter 34 40,000
Name
Harry
Sally
George
Helena
Peter
Employee
∏ name(Employee)
13. Project Operator Example
Name Age Salary
Harry 34 80,000
Sally 28 90,000
George 29 70,000
Helena 54 54,280
Peter 34 40,000
Name
Sally
Employee бSalary>80,000(Employee)
Name Age Salary
Sally 28 90,000
∏ name(бSalary>80,000(Employee))
14. 14
Example - Projection
Produce a list of salaries for all staff, showing only
staffNo, fName, lName, and salary details.
ΠstaffNo, fName,lName, salary(Staff)
15. Union, Intersection, Set-Difference
All of these operations take two input relations, which
must be union-compatible:
– Same number of fields.
– `Corresponding’ fields have the same type.
15
16. 16
Union
R ∪ S
– Union of two relations R and S defines a relation
that contains all the tuples of R, or S, or both R
and S, duplicate tuples being eliminated.
– R and S must be union-compatible.
If R and S have I and J tuples, respectively, union
is obtained by concatenating them into one relation
with a maximum of (I + J) tuples.
17. Union Operator Example
FN LN
Susan Yao
Ramesh Shah
Barbara Jones
Amy Ford
Jimmy Wang
FN LN
John Smith
Ricardo Brown
Susan Yao
Francis Johnson
Ramesh Shah
Student Professor
FN LN
Susan Yao
Ramesh Shah
Barbara Jones
Amy Ford
Jimmy Wang
John Smith
Ricardo Brown
Francis Johnson
Student U Professor
18. 18
Example - Union
List all cities where there is either a branch office
or a property for rent.
Πcity(Branch) ∪ Πcity(PropertyForRent)
19. 19
Set Difference
R – S
– Defines a relation consisting of the tuples that
are in relation R, but not in S.
– R and S must be union-compatible.
20. Set Difference Operator Example
FN LN
Susan Yao
Ramesh Shah
Barbara Jones
Amy Ford
Jimmy Wang
FN LN
John Smith
Ricardo Brown
Susan Yao
Francis Johnson
Ramesh Shah
Student
Professor
FN LN
Barbara Jones
Amy Ford
Jimmy Wang
Student - Professor
FN LN
John Smith
Ricardo Brown
Francis Johnson
Professor - Student
21. 21
Example - Set Difference
List all cities where there is a branch office but no
properties for rent.
Πcity(Branch) – Πcity(PropertyForRent)
22. 22
Intersection
R ∩ S
– Defines a relation consisting of the set of all
tuples that are in both R and S.
– R and S must be union-compatible.
Expressed using basic operations:
R ∩ S = R – (R – S)
23. Intersection Operator Example
FN LN
Susan Yao
Ramesh Shah
Barbara Jones
Amy Ford
Jimmy Wang
FN LN
John Smith
Ricardo Brown
Susan Yao
Francis Johnson
Ramesh Shah
Student Professor
FN LN
Susan Yao
Ramesh Shah
Student ∩ Professor
24. 24
Example - Intersection
List all cities where there is both a branch office
and at least one property for rent.
Πcity(Branch) ∩ Πcity(PropertyForRent)
25. 25
Cartesian product
R X S
– Defines a relation that is the concatenation of
every tuple of relation R with every tuple of
relation S.
26. 26
Example - Cartesian product
List the names and comments of all clients who have
viewed a property for rent.
(ΠclientNo,fName,lName(Client)) X (ΠclientNo,propertyNo,comment(Viewing))
27. 27
Example - Cartesian product and Selection
Use selection operation to extract those tuples where
Client.clientNo = Viewing.clientNo.
σClient.clientNo=Viewing.clientNo((∏clientNo,fName, lName(Client)) Χ (∏clientNo, propertyNo,
comment(Viewing)))
Cartesian product and Selection can be reduced to a single
operation called a Join.
28. 28
Join Operations
Join is a derivative of Cartesian product.
Equivalent to performing a Selection, using join
predicate as selection formula, over Cartesian
product of the two operand relations.
One of the most difficult operations to implement
efficiently in an RDBMS and one reason why
RDBMSs have intrinsic performance problems.
29. 29
Join Operations
Various forms of join operation
– Theta join
– Equijoin (a particular type of Theta join)
– Natural join
– Outer join
– Semijoin
30. 30
Theta join (θ-join)
R FS
– Defines a relation that contains tuples
satisfying the predicate F from the Cartesian
product of R and S.
– The predicate F is of the form R.ai θ S.bi
where θ may be one of the comparison
operators (<, ≤, >, ≥, =, ≠).
31. 31
Theta join (θ-join)
Can rewrite Theta join using basic Selection and
Cartesian product operations.
R FS = σF(R Χ S)
Degree of a Theta join is sum of degrees of the
operand relations R and S. If predicate F contains
only equality (=), the term Equijoin is used.
32. 32
Example - Equijoin
List the names and comments of all clients who
have viewed a property for rent.
(ΠclientNo,fName, lName(Client)) Client.clientNo=Viewing.clientNo (ΠclientNo, propertyNo,
comment(Viewing))
33. 33
Natural join
R S
– An Equijoin of the two relations R and S over all
common attributes x. One occurrence of each
common attribute is eliminated from the result.
34. 34
Example - Natural join
List the names and comments of all clients who
have viewed a property for rent.
(ΠclientNo, fName,lName(Client))
(ΠclientNo,propertyNo, comment(Viewing))
35. 35
Outer join
To display rows in the result that do not have
matching values in the join column, use Outer
join.
R S
– (Left) outer join is join in which tuples from
R that do not have matching values in
common columns of S are also included in
result relation.
36. 36
Example - Left Outer join
Produce a status report on property viewings.
ΠpropertyNo, street, city(PropertyForRent)
Viewing
37. 37
Semijoin
R FS
– Defines a relation that contains the tuples of R that
participate in the join of R with S.
– It performs a join on two relations and then project
Over the attributes of first operand.
Can rewrite Semijoin using Projection and Join:
R F S = ΠA(R F S)
38. 38
Example - Semijoin
List complete details of all staff who work at the
branch in Glasgow.
Staff Staff.branchNo=Branch.branchNo(σcity=‘Glasgow’(Branch))
39. 39
Division
R ÷ S
– Defines a relation over the attributes C that consists of
set of tuples from R that match combination of every
tuple in S.
Expressed using basic operations:
T1 ← ΠC(R)
T2 ← ΠC((S X T1) – R)
T ← T1 – T2
40. 40
Example - Division
Identify all clients who have viewed all properties
with three rooms.
(ΠclientNo,propertyNo(Viewing)) ÷
(ΠpropertyNo(σrooms=3 (PropertyForRent)))
41. Relational DBMS
The following tables form part of a database held in a
relational DBMS:
Hotel (hotelNo, hotelName, city)
Room (roomNo, hotelNo, type, price)
Booking (hotelNo, guestNo, dateFrom, dateTo,
roomNo)
Guest (guestNo, guestName, guestAddress)
41
43. Exercise – Gererate Relational algebra
List all hotels.
List all single rooms with a price below £20 per night.
List the names and cities of all guests.
List the price and type of all rooms at the Grosvenor
Hotel.
List all guests currently staying at the Grosvenor Hotel.
List the details of all rooms at the Grosvenor Hotel,
including the name of the guest staying in the room, if
the room is occupied.
List the guest details (guestNo, guestName, and
guestAddress) of all guests staying at the Grosvenor
43
44. SQL
Structured Query Language (SQL)
– Standardised by ANSI
– Supported by modern RDBMSs
Commands fall into three groups
– Data Definition Language (DLL)
» Create tables, etc
– Data Manipulation Language (DML)
» Retrieve and modify data
– Data Control Language
» Control what users can do – grant and revoke privileges
44