The document describes the relational model for databases. It defines key concepts like relations, attributes, tuples, domains, schemas, keys, and foreign keys. It explains basic relational algebra operations like select, project, join, union, difference, and cartesian product. It provides examples of how to express queries using these operations on sample relations from a banking database. Additional relational algebra operations like rename, intersection, and natural join are also introduced.
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.
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.
The document describes the relational model for relational databases. It discusses the structure of relational databases including relations, tuples, attributes, domains, keys and relation schemas. It also describes the relational algebra query language including operators like select, project, join, union and set differences. Examples are provided to illustrate how to write queries using these operators to retrieve and manipulate data from relations that model real-world entities and relationships, like customers, accounts and loans in a banking example.
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.
This document provides an overview of the relational model and relational database concepts. It defines key terms like relation schemas, attributes, domains, tuples, keys, and foreign keys. It also explains relational algebra operations like select, project, join, and aggregate functions. Examples of relational queries on banking database relations like accounts, customers, loans are provided to illustrate concepts. The document is intended to help students learn and practice relational database and query language concepts.
This document discusses the relational model and its core concepts. It defines relations as sets of tuples with attributes, and describes relation schemas, instances, keys such as candidate keys and foreign keys. It also introduces the concept of a relational database containing multiple relations and discusses query languages for retrieving information from the database.
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.
The document discusses the relational model of databases and relational algebra operations. It covers:
1. The basic structure of relations including attributes, tuples, domains, and relation schemas.
2. Key relational algebra operations like selection, projection, union, set difference, and cartesian product and provides examples of each.
3. Additional topics like keys, foreign keys, query languages, and examples of queries using relational algebra on banking database relations.
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.
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.
The document describes the relational model for relational databases. It discusses the structure of relational databases including relations, tuples, attributes, domains, keys and relation schemas. It also describes the relational algebra query language including operators like select, project, join, union and set differences. Examples are provided to illustrate how to write queries using these operators to retrieve and manipulate data from relations that model real-world entities and relationships, like customers, accounts and loans in a banking example.
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.
This document provides an overview of the relational model and relational database concepts. It defines key terms like relation schemas, attributes, domains, tuples, keys, and foreign keys. It also explains relational algebra operations like select, project, join, and aggregate functions. Examples of relational queries on banking database relations like accounts, customers, loans are provided to illustrate concepts. The document is intended to help students learn and practice relational database and query language concepts.
This document discusses the relational model and its core concepts. It defines relations as sets of tuples with attributes, and describes relation schemas, instances, keys such as candidate keys and foreign keys. It also introduces the concept of a relational database containing multiple relations and discusses query languages for retrieving information from the database.
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.
The document discusses the relational model of databases and relational algebra operations. It covers:
1. The basic structure of relations including attributes, tuples, domains, and relation schemas.
2. Key relational algebra operations like selection, projection, union, set difference, and cartesian product and provides examples of each.
3. Additional topics like keys, foreign keys, query languages, and examples of queries using relational algebra on banking database relations.
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 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 discusses relational algebra and accessing MySQL databases. It covers fundamental relational algebra operations like selection, projection, union, set difference, and cartesian product. It also describes how to create tables in a MySQL database, submit SQL queries, and access the database from the command line. Basic concepts like relations, attributes, tuples, schemas, and keys are defined. Examples are provided to illustrate relational algebra operations.
This document discusses query languages in database management systems. It covers the main categories of query languages: procedural languages like relational algebra, and non-procedural languages like tuple and domain relational calculus. Relational algebra operators like selection, projection, union, and join are defined. Example queries are provided in both relational algebra and relational calculus formats. Functional dependencies, candidate keys, and the closure of attribute sets under a set of functional dependencies are also explained.
This document provides an overview of SQL (Structured Query Language) including its history, data definition and manipulation capabilities. Key topics covered include SQL's data types, basic queries using SELECT, FROM and WHERE clauses, joins, aggregation, null values, triggers and indexes. The document also discusses SQL standards over time and commercial database implementations of SQL features.
CHAPTER 2 DBMS IN EASY WAY BY MILAN PATELShashi Patel
The document discusses the relational model for database management. It provides details on the structure of relational databases including domains, relations, and schemas. It describes fundamental relational algebra operations like selection, projection, join, and set operations. It also covers tuple and domain relational calculus as non-procedural query languages. The document provides examples of queries and operations on sample relations to illustrate relational concepts.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
This document provides a summary of the basic structure and key concepts of SQL, including:
1) SQL queries typically involve a SELECT statement to retrieve attributes from one or more relations based on conditions in a WHERE clause.
2) Common SQL clauses include SELECT, FROM, WHERE, GROUP BY, HAVING, and aggregate functions are used to perform calculations on groups of records.
3) Null values, three valued logic, and handling of duplicates are important concepts when working with SQL queries and relations.
The document provides an overview of the relational model used in database management systems. It defines key concepts like relations, attributes, tuples, domains, schemas, keys, and foreign keys. It also describes common relational algebra operations like select, project, join, union, and set differences. Examples are provided to illustrate how these operations work on relations. Additional topics covered include query languages, normalization, and modeling a banking example database using these concepts.
This chapter discusses SQL and relational database concepts. It covers the basic structure of SQL queries involving SELECT, FROM, and WHERE clauses. It describes key SQL features like aggregate functions, null values, nested subqueries, and modification of databases through insertion, deletion, and updating of records. The document provides examples to illustrate concepts like joins, grouping, ordering results, and use of comparison and string operators in SQL queries.
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.
This document discusses relational algebra operations including select, project, union, set difference, cartesian product, and rename. It provides examples of how to write queries using these operations on sample relations like loan and borrower. The select operation filters tuples based on a predicate, project returns a relation with some attributes removed, union combines the tuples from two relations, set difference returns tuples in one relation not in another, cartesian product combines all tuples from two relations, and rename assigns a new name to the result of an expression. The goal is to help students understand relational algebra and how to write queries using its basic operations.
relational algebra and calculus queries .pptShahidSultan24
This document provides an overview of key concepts in the relational model of databases, including:
- Relations are represented as tables with rows (tuples) and columns (attributes). The order of tuples and attributes is not important.
- A relational database contains multiple relations that each store a part of the overall database information. Keys are used to identify unique tuples.
- Relational algebra defines operations like select, project, join, and set operations that can be used to query and manipulate relations. Operations take relations as input and produce new relations as output.
The document discusses relational algebra and tuple relational calculus. It describes relational algebra as a procedural query language that uses unary operations like select and project and binary operations like join and union to derive result relations from existing relations. It then explains tuple relational calculus as a nonprocedural query language where queries return tuples satisfying a predicate. Examples of queries using both languages on banking relations are provided to illustrate their usage.
The document discusses relational algebra and relational query languages. It provides an overview of SQL and relational algebra, including their differences. It describes the basic relational algebra operators like select, project, union, and join. It provides examples of how to express queries using these operators and how relational algebra expressions are used internally by databases to evaluate SQL queries.
The document discusses tuple relational calculus and domain relational calculus. Tuple relational calculus describes the desired information as a set of tuples that satisfy a predicate, in the form {t | P(t)}. Domain relational calculus uses domain variables that take on attribute values rather than entire tuples. Both languages use atoms, formulae, and quantification to write queries. Expressions must be safe by only generating tuples within the domain to avoid infinite relations. Sample queries are provided to illustrate the languages.
This document provides an introduction to relational calculus, including tuple relational calculus (TRC) and domain relational calculus (DRC). It defines relational calculus as a non-procedural query language that specifies what to do without explaining how. TRC works on tuples and allows selecting tuples that satisfy a condition, while DRC works on domains/attributes and allows selecting attributes. Examples of queries are provided for each that filter relations based on conditions.
Student Voting Application for Election – Using SMS (1).pptxShivareddyGangam
This document outlines a proposed system for student voting using SMS OTP verification. The proposed system aims to address disadvantages of existing paper-based voting systems like fake voting and long queues. It would allow students to register and vote through a mobile or web application, receiving an OTP to verify their identity before casting their vote. The system aims to reduce costs and time spent on voting while increasing security, participation and accuracy of results. Key modules include user login, OTP verification, candidate selection and vote submission for users and election configuration, voter list management and results for admins.
The document discusses various aspects of software project management including the management scope, people, product, process, and project. It also covers the W5HH principle for defining key project characteristics including why the system is being developed, what will be done, when it will be done, who is responsible, where people are located, how the job will be done technically and authoritatively, and how many resources are needed. Finally, it discusses software metrics for measuring aspects like size, quality, and productivity.
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 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 discusses relational algebra and accessing MySQL databases. It covers fundamental relational algebra operations like selection, projection, union, set difference, and cartesian product. It also describes how to create tables in a MySQL database, submit SQL queries, and access the database from the command line. Basic concepts like relations, attributes, tuples, schemas, and keys are defined. Examples are provided to illustrate relational algebra operations.
This document discusses query languages in database management systems. It covers the main categories of query languages: procedural languages like relational algebra, and non-procedural languages like tuple and domain relational calculus. Relational algebra operators like selection, projection, union, and join are defined. Example queries are provided in both relational algebra and relational calculus formats. Functional dependencies, candidate keys, and the closure of attribute sets under a set of functional dependencies are also explained.
This document provides an overview of SQL (Structured Query Language) including its history, data definition and manipulation capabilities. Key topics covered include SQL's data types, basic queries using SELECT, FROM and WHERE clauses, joins, aggregation, null values, triggers and indexes. The document also discusses SQL standards over time and commercial database implementations of SQL features.
CHAPTER 2 DBMS IN EASY WAY BY MILAN PATELShashi Patel
The document discusses the relational model for database management. It provides details on the structure of relational databases including domains, relations, and schemas. It describes fundamental relational algebra operations like selection, projection, join, and set operations. It also covers tuple and domain relational calculus as non-procedural query languages. The document provides examples of queries and operations on sample relations to illustrate relational concepts.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
The document discusses the relational model for databases including:
1) The structure of relational databases including relations, tuples, attributes, domains, relation schemas, and relation instances.
2) Relational algebra which is a procedural query language using operators like select, project, join, and set operations.
3) Additional concepts like keys, normalization, and an example banking schema to demonstrate relational queries.
This document provides a summary of the basic structure and key concepts of SQL, including:
1) SQL queries typically involve a SELECT statement to retrieve attributes from one or more relations based on conditions in a WHERE clause.
2) Common SQL clauses include SELECT, FROM, WHERE, GROUP BY, HAVING, and aggregate functions are used to perform calculations on groups of records.
3) Null values, three valued logic, and handling of duplicates are important concepts when working with SQL queries and relations.
The document provides an overview of the relational model used in database management systems. It defines key concepts like relations, attributes, tuples, domains, schemas, keys, and foreign keys. It also describes common relational algebra operations like select, project, join, union, and set differences. Examples are provided to illustrate how these operations work on relations. Additional topics covered include query languages, normalization, and modeling a banking example database using these concepts.
This chapter discusses SQL and relational database concepts. It covers the basic structure of SQL queries involving SELECT, FROM, and WHERE clauses. It describes key SQL features like aggregate functions, null values, nested subqueries, and modification of databases through insertion, deletion, and updating of records. The document provides examples to illustrate concepts like joins, grouping, ordering results, and use of comparison and string operators in SQL queries.
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.
This document discusses relational algebra operations including select, project, union, set difference, cartesian product, and rename. It provides examples of how to write queries using these operations on sample relations like loan and borrower. The select operation filters tuples based on a predicate, project returns a relation with some attributes removed, union combines the tuples from two relations, set difference returns tuples in one relation not in another, cartesian product combines all tuples from two relations, and rename assigns a new name to the result of an expression. The goal is to help students understand relational algebra and how to write queries using its basic operations.
relational algebra and calculus queries .pptShahidSultan24
This document provides an overview of key concepts in the relational model of databases, including:
- Relations are represented as tables with rows (tuples) and columns (attributes). The order of tuples and attributes is not important.
- A relational database contains multiple relations that each store a part of the overall database information. Keys are used to identify unique tuples.
- Relational algebra defines operations like select, project, join, and set operations that can be used to query and manipulate relations. Operations take relations as input and produce new relations as output.
The document discusses relational algebra and tuple relational calculus. It describes relational algebra as a procedural query language that uses unary operations like select and project and binary operations like join and union to derive result relations from existing relations. It then explains tuple relational calculus as a nonprocedural query language where queries return tuples satisfying a predicate. Examples of queries using both languages on banking relations are provided to illustrate their usage.
The document discusses relational algebra and relational query languages. It provides an overview of SQL and relational algebra, including their differences. It describes the basic relational algebra operators like select, project, union, and join. It provides examples of how to express queries using these operators and how relational algebra expressions are used internally by databases to evaluate SQL queries.
The document discusses tuple relational calculus and domain relational calculus. Tuple relational calculus describes the desired information as a set of tuples that satisfy a predicate, in the form {t | P(t)}. Domain relational calculus uses domain variables that take on attribute values rather than entire tuples. Both languages use atoms, formulae, and quantification to write queries. Expressions must be safe by only generating tuples within the domain to avoid infinite relations. Sample queries are provided to illustrate the languages.
This document provides an introduction to relational calculus, including tuple relational calculus (TRC) and domain relational calculus (DRC). It defines relational calculus as a non-procedural query language that specifies what to do without explaining how. TRC works on tuples and allows selecting tuples that satisfy a condition, while DRC works on domains/attributes and allows selecting attributes. Examples of queries are provided for each that filter relations based on conditions.
Student Voting Application for Election – Using SMS (1).pptxShivareddyGangam
This document outlines a proposed system for student voting using SMS OTP verification. The proposed system aims to address disadvantages of existing paper-based voting systems like fake voting and long queues. It would allow students to register and vote through a mobile or web application, receiving an OTP to verify their identity before casting their vote. The system aims to reduce costs and time spent on voting while increasing security, participation and accuracy of results. Key modules include user login, OTP verification, candidate selection and vote submission for users and election configuration, voter list management and results for admins.
The document discusses various aspects of software project management including the management scope, people, product, process, and project. It also covers the W5HH principle for defining key project characteristics including why the system is being developed, what will be done, when it will be done, who is responsible, where people are located, how the job will be done technically and authoritatively, and how many resources are needed. Finally, it discusses software metrics for measuring aspects like size, quality, and productivity.
Principal Components Analysis (PCA) is an exploratory technique used to reduce the dimensionality of data sets while retaining as much information as possible. It transforms a number of correlated variables into a smaller number of uncorrelated variables called principal components. PCA is commonly used for applications like face recognition, image compression, and gene expression analysis by reducing the dimensions of large data sets and finding patterns in the data.
Software engineering is the application of engineering principles and methods to the development of software. It involves developing software products using well-defined scientific principles, methods, and procedures. The role of software has evolved significantly over the past 50 years from standalone programs to complex systems that deliver both information and control functions. Addressing the "software crisis" of the 1960s required treating software development as an engineering discipline with processes, documentation, and quality assurance rather than an art. Applying software engineering principles and practices was seen as a solution to issues like projects running over budget and schedule, producing inefficient and low-quality software that did not meet requirements.
1. Topological sort is a method for arranging the vertices of a directed acyclic graph (DAG) in linear order such that if vertex A appears before vertex B, then there is no directed edge from B to A.
2. A topological sort is not unique - there can be multiple valid linear orderings of the vertices. An algorithm uses the in-degrees of vertices to iteratively select vertices with no incoming edges until a full ordering is produced.
3. The document provides an example topological sort and discusses implementation using an in-degree array and priority queue to track vertices eligible for the ordering.
Principal component analysis (PCA) is used to reduce the dimensionality of data while retaining as much information as possible. It identifies the underlying factors or components that explain the variance in a data set. PCA works by finding the directions with the most variance in the data and projecting the data onto these principal components. This results in a smaller set of dimensions that capture most of the information in the original high-dimensional data. PCA is commonly used for dimensionality reduction before applying other machine learning algorithms like classification or clustering.
This presentation discusses rational agents and rationality. It defines rationality as acting reasonably and with good judgment to achieve goals. A rational agent selects actions that are expected to maximize its performance given its percepts and prior knowledge. Rational agents use functions to map percept sequences to actions. Examples discussed include a delivery robot, diagnostic system, trading agent, and tutoring system. The presentation also covers the key components of agents, including performance measures, environments, actuators, sensors, and the PEAS framework. It concludes that building more autonomous rational agents with enhanced learning abilities can satisfy the growing demands of artificial intelligence.
Artificial intelligence has great potential to revolutionize healthcare. It can help predict ICU transfers and hospital readmissions by identifying at-risk patients from their medical data. AI is also used in medical testing through new methods like bloodless blood testing using smartphone ECGs. It improves clinical workflows by reducing physician burnout through tools like vein finders. AI helps prevent infections by monitoring patients for early signs of sepsis or other healthcare-acquired infections. During the COVID-19 pandemic, AI has assisted with tracking and forecasting outbreaks, diagnosing patients, processing health claims, and developing new drugs to treat the virus.
The document discusses software risk management and types of risks in software development projects. It identifies five main types of risks: schedule risks, budget risks, operational risks, technical risks, and programmatic risks. It then describes various tools and techniques for risk identification, assessment, and management, including documentation reviews, brainstorming, risk tables, and risk monitoring. Effective communication is also highlighted as important for coordinating the project team and managing risks.
This document introduces combinatorial structures, algorithms, and problems. It defines combinatorial structures like graphs, hypergraphs, and partitions. It explains that combinatorial algorithms are used for generation, enumeration, and search of combinatorial structures. Generation constructs all structures of a type, enumeration computes their number, and search finds examples. It notes that many search and optimization problems are NP-hard or NP-complete, meaning they are very difficult to solve efficiently.
This document discusses various applications of ICT in healthcare, including artificial intelligence, automation technologies, 3D printing, and micro 3D printing. It describes how AI is used in healthcare applications like clinical expert systems, gaming, and medical imaging. It also outlines benefits of automating healthcare administration tasks like billing, scheduling and electronic health records. Finally, it provides details on how 3D printing and micro 3D printing are used to create medical devices and components for applications in microfluidics.
This document discusses the use of information and communication technologies (ICT) in healthcare. It begins with an introduction to ICT technologies and their role in healthcare. It then discusses how ICT is used in healthcare for health education, hospital management systems, health research, and health data management. The document also discusses future and emerging ICT technologies like augmented reality, virtual reality, artificial intelligence, and robotic process automation and how they are impacting healthcare. It concludes by discussing the use of computer simulations for healthcare education and training, including examples of simulations used for intra-oral radiography, cervical spine procedures, and surgical training.
Strassen's algorithm improves upon the basic matrix multiplication algorithm. It can multiply two N x N matrices using only 7 multiplications instead of the 8 required by basic multiplication. This is accomplished by dividing the matrices into sub-matrices and recursively applying a divide-and-conquer approach to multiply the sub-matrices using fewer operations than basic multiplication through combinations of additions, subtractions, and multiplications. The algorithm provides an asymptotic improvement over basic matrix multiplication.
The document discusses algorithms and their importance in computer science. It defines an algorithm as a set of clearly defined instructions to solve a problem. Key properties algorithms must satisfy are being unambiguous, terminating in a finite number of steps, and being implementable. The document then discusses the greatest common divisor (GCD) algorithm as an example, outlining its pseudocode, correctness, and time complexity of O(log n). It notes the relationship between algorithms and data structures, stating the way data is organized can impact algorithm efficiency.
Automated testing involves developing and executing test scripts using an automated test tool to verify test requirements. It has advantages like reduced costs, increased efficiency, and improved quality. However, automated testing also has limitations such as an inability to test certain aspects that require physical interaction. The automated test life-cycle methodology involves planning, designing, executing, and reviewing automated tests. Key steps include deciding what to automate, acquiring suitable tools, and analyzing the testing process.
The document summarizes a presentation on transforming manual testing processes to incorporate test automation. It discusses automation models and frameworks, incorporating automation into standard testing processes, typical test activities and deliverables for agile and waterfall SDLCs. It provides a deep dive into testing artifacts and how to better plan for automation. It also presents a case study where a command-driven testing framework was adopted to help automate manual testing processes and limit concurrent work by testers on the same templates. The presentation aims to provide best practices for planning and implementing test automation.
The document discusses various aspects of software project management including the management scope, people, product, process, and project. It also covers software metrics which are quantitative measures used to gain insight into software characteristics and processes. Examples of metrics include lines of code, defects, and customer problems to measure aspects like size, quality, and satisfaction.
This document describes a proposed mobile application called Farmer Helper that aims to provide farmers with important agricultural information. The app would give details on suitable crops for different seasons and regions, pesticide and fertilizer schedules, weather alerts, and information on government loans and schemes. It seeks to address issues like lack of knowledge on modern farming practices and difficulties in exchanging information. The app is meant to help farmers make better decisions and efficiently buy/sell agricultural goods and equipment through its modules for farmers, marketing, and citizens. It could bridge the technology gap for farmers and benefit all sectors involved in farming.
The document discusses different types of intelligent agents and environments. It defines agents as anything that can perceive its environment and act upon it. Agents are described according to their performance measure, environment, actuators, and sensors (PEAS). Environments can be fully/partially observable, deterministic/stochastic, episodic/sequential, static/dynamic, discrete/continuous, and single-agent/multi-agent. Four types of agents are described - simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Learning agents are also introduced.
This document provides an overview of machine learning applications and concepts. It discusses different problem types in machine learning like regression, classification, and optimization. It also covers key concepts like neural networks, data collection and modeling, prediction, and case studies. As an example, it analyzes a case study that uses different machine learning algorithms like linear regression, random forests, and support vector machines to predict the compressive strength of concrete samples based on features like cement, water, and age. The models are evaluated on their ability to accurately predict compressive strength values for previously unseen test data.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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2. Relational Model
Structure of Relational Databases
Fundamental Relational-Algebra-Operations
Additional Relational-Algebra-Operations
Extended Relational-Algebra-Operations
Null Values
Modification of the Database
4. Basic Structure
Formally, given sets D1, D2, …. Dn a relation r is a subset of
D1 x D2 x … x Dn
Thus, a relation is a set of n-tuples (a1, a2, …, an) where each ai Di
Example: If
customer_name = {Jones, Smith, Curry, Lindsay, …}
/* Set of all customer names */
customer_street = {Main, North, Park, …} /* set of all street names*/
customer_city = {Harrison, Rye, Pittsfield, …} /* set of all city names */
Then r = { (Jones, Main, Harrison),
(Smith, North, Rye),
(Curry, North, Rye),
(Lindsay, Park, Pittsfield) }
is a relation over
customer_name x customer_street x customer_city
5. Attribute Types
Each attribute of a relation has a name
The set of allowed values for each attribute is called the domain of the
attribute
Attribute values are (normally) required to be atomic; that is, indivisible
E.g. the value of an attribute can be an account number,
but cannot be a set of account numbers
Domain is said to be atomic if all its members are atomic
The special value null is a member of every domain
The null value causes complications in the definition of many operations
We shall ignore the effect of null values in our main presentation
and consider their effect later
6. Relation Schema
A1, A2, …, An are attributes
R = (A1, A2, …, An ) is a relation schema
Example:
Customer_schema = (customer_name, customer_street, customer_city)
r(R) denotes a relation r on the relation schema R
Example:
customer (Customer_schema)
7. Relation Instance
The current values (relation instance) of a relation are specified by
a table
An element t of r is a tuple, represented by a row in a table
Jones
Smith
Curry
Lindsay
customer_name
Main
North
North
Park
customer_street
Harrison
Rye
Rye
Pittsfield
customer_city
customer
attributes
(or columns)
tuples
(or rows)
8. Relations are Unordered
Order of tuples is irrelevant (tuples may be stored in an arbitrary order)
Example: account relation with unordered tuples
9. Database
A database consists of multiple relations
Information about an enterprise is broken up into parts, with each relation
storing one part of the information
account : stores information about accounts
depositor : stores information about which customer
owns which account
customer : stores information about customers
Storing all information as a single relation such as
bank(account_number, balance, customer_name, ..)
results in
repetition of information
e.g.,if two customers own an account (What gets repeated?)
the need for null values
e.g., to represent a customer without an account
Normalization theory (Chapter 7) deals with how to design relational schemas
12. Keys
Let K R
K is a superkey of R if values for K are sufficient to identify a unique tuple of
each possible relation r(R)
by “possible r ” we mean a relation r that could exist in the enterprise we
are modeling.
Example: {customer_name, customer_street} and
{customer_name}
are both superkeys of Customer, if no two customers can possibly have
the same name
In real life, an attribute such as customer_id would be used instead of
customer_name to uniquely identify customers, but we omit it to keep
our examples small, and instead assume customer names are unique.
13. Keys (Cont.)
K is a candidate key if K is minimal
Example: {customer_name} is a candidate key for Customer, since it
is a superkey and no subset of it is a superkey.
Primary key: a candidate key chosen as the principal means of
identifying tuples within a relation
Should choose an attribute whose value never, or very rarely,
changes.
E.g. email address is unique, but may change
14. Foreign Keys
A relation schema may have an attribute that corresponds to the primary
key of another relation. The attribute is called a foreign key.
E.g. customer_name and account_number attributes of depositor are
foreign keys to customer and account respectively.
Only values occurring in the primary key attribute of the referenced
relation may occur in the foreign key attribute of the referencing
relation.
Schema diagram
15. Query Languages
Language in which user requests information from the database.
Categories of languages
Procedural
Non-procedural, or declarative
“Pure” languages:
Relational algebra
Tuple relational calculus
Domain relational calculus
Pure languages form underlying basis of query languages that people
use.
16. Relational Algebra
Procedural language
Six basic operators
select:
project:
union:
set difference: –
Cartesian product: x
rename:
The operators take one or two relations as inputs and produce a new
relation as a result.
17. Select Operation – Example
Relation r
A B C D
1
5
12
23
7
7
3
10
A=B ^ D > 5 (r)
A B C D
1
23
7
10
18. Select Operation
Notation: p(r)
p is called the selection predicate
Defined as:
p(r) = {t | t r and p(t)}
Where p is a formula in propositional calculus consisting of terms
connected by : (and), (or), (not)
Each term is one of:
<attribute> op <attribute> or <constant>
where op is one of: =, , >, . <.
Example of selection:
branch_name=“Perryridge”(account)
19. Project Operation – Example
Relation r: A B C
10
20
30
40
1
1
1
2
A C
1
1
1
2
=
A C
1
1
2
A,C (r)
20. Project Operation
Notation:
where A1, A2 are attribute names and r is a relation name.
The result is defined as the relation of k columns obtained by erasing
the columns that are not listed
Duplicate rows removed from result, since relations are sets
Example: To eliminate the branch_name attribute of account
account_number, balance (account)
)
(
,
,
, 2
1
r
k
A
A
A
21. Union Operation – Example
Relations r, s:
r s:
A B
1
2
1
A B
2
3
r
s
A B
1
2
1
3
22. Union Operation
Notation: r s
Defined as:
r s = {t | t r or t s}
For r s to be valid.
1. r, s must have the same arity (same number of attributes)
2. The attribute domains must be compatible (example: 2nd column
of r deals with the same type of values as does the 2nd
column of s)
Example: to find all customers with either an account or a loan
customer_name (depositor) customer_name (borrower)
23. Set Difference Operation – Example
Relations r, s:
r – s:
A B
1
2
1
A B
2
3
r
s
A B
1
1
24. Set Difference Operation
Notation r – s
Defined as:
r – s = {t | t r and t s}
Set differences must be taken between compatible
relations.
r and s must have the same arity
attribute domains of r and s must be compatible
25. Cartesian-Product Operation – Example
Relations r, s:
r x s:
A B
1
2
A B
1
1
1
1
2
2
2
2
C D
10
10
20
10
10
10
20
10
E
a
a
b
b
a
a
b
b
C D
10
10
20
10
E
a
a
b
b
r
s
26. Cartesian-Product Operation
Notation r x s
Defined as:
r x s = {t q | t r and q s}
Assume that attributes of r(R) and s(S) are disjoint. (That is, R S = ).
If attributes of r(R) and s(S) are not disjoint, then renaming must be
used.
27. Composition of Operations
Can build expressions using multiple operations
Example: A=C(r x s)
r x s
A=C(r x s)
A B
1
1
1
1
2
2
2
2
C D
10
10
20
10
10
10
20
10
E
a
a
b
b
a
a
b
b
A B C D E
1
2
2
10
10
20
a
a
b
28. Rename Operation
Allows us to name, and therefore to refer to, the results of relational-
algebra expressions.
Allows us to refer to a relation by more than one name.
Example:
x (E)
returns the expression E under the name X
If a relational-algebra expression E has arity n, then
returns the result of expression E under the name X, and with the
attributes renamed to A1 , A2 , …., An .
)
(
)
,...,
,
( 2
1
E
n
A
A
A
x
30. Example Queries
Find all loans of over $1200
Find the loan number for each loan of an amount greater than
$1200
amount > 1200 (loan)
loan_number (amount > 1200 (loan))
Find the names of all customers who have a loan, an account, or both,
from the bank
customer_name (borrower) customer_name (depositor)
31. Example Queries
Find the names of all customers who have a loan at the Perryridge
branch.
Find the names of all customers who have a loan at the
Perryridge branch but do not have an account at any branch of
the bank.
customer_name (branch_name = “Perryridge”
(borrower.loan_number = loan.loan_number(borrower x loan))) –
customer_name(depositor)
customer_name (branch_name=“Perryridge”
(borrower.loan_number = loan.loan_number(borrower x loan)))
32. Example Queries
Find the names of all customers who have a loan at the Perryridge branch.
Query 2
customer_name(loan.loan_number = borrower.loan_number (
(branch_name = “Perryridge” (loan)) x borrower))
Query 1
customer_name (branch_name = “Perryridge” (
borrower.loan_number = loan.loan_number (borrower x loan)))
33. Example Queries
Find the largest account balance
Strategy:
Find those balances that are not the largest
– Rename account relation as d so that we can compare each
account balance with all others
Use set difference to find those account balances that were not found
in the earlier step.
The query is:
balance(account) - account.balance
(account.balance < d.balance (account x d (account)))
34. Formal Definition
A basic expression in the relational algebra consists of either one of the
following:
A relation in the database
A constant relation
Let E1 and E2 be relational-algebra expressions; the following are all
relational-algebra expressions:
E1 E2
E1 – E2
E1 x E2
p (E1), P is a predicate on attributes in E1
s(E1), S is a list consisting of some of the attributes in E1
x (E1), x is the new name for the result of E1
35. Additional Operations
We define additional operations that do not add any power to the
relational algebra, but that simplify common queries.
Set intersection
Natural join
Division
Assignment
36. Set-Intersection Operation
Notation: r s
Defined as:
r s = { t | t r and t s }
Assume:
r, s have the same arity
attributes of r and s are compatible
Note: r s = r – (r – s)
38. Notation: r s
Natural-Join Operation
Let r and s be relations on schemas R and S respectively.
Then, r s is a relation on schema R S obtained as follows:
Consider each pair of tuples tr from r and ts from s.
If tr and ts have the same value on each of the attributes in R S, add
a tuple t to the result, where
t has the same value as tr on r
t has the same value as ts on s
Example:
R = (A, B, C, D)
S = (E, B, D)
Result schema = (A, B, C, D, E)
r s is defined as:
r.A, r.B, r.C, r.D, s.E (r.B = s.B r.D = s.D (r x s))
39. Natural Join Operation – Example
Relations r, s:
A B
1
2
4
1
2
C D
a
a
b
a
b
B
1
3
1
2
3
D
a
a
a
b
b
E
r
A B
1
1
1
1
2
C D
a
a
a
a
b
E
s
r s
40. Division Operation
Notation:
Suited to queries that include the phrase “for all”.
Let r and s be relations on schemas R and S respectively
where
R = (A1, …, Am , B1, …, Bn )
S = (B1, …, Bn)
The result of r s is a relation on schema
R – S = (A1, …, Am)
r s = { t | t R-S (r) u s ( tu r ) }
Where tu means the concatenation of tuples t and u to
produce a single tuple
r s
41. Division Operation – Example
Relations r, s:
r s: A
B
1
2
A B
1
2
3
1
1
1
3
4
6
1
2
r
s
42. Another Division Example
A B
a
a
a
a
a
a
a
a
C D
a
a
b
a
b
a
b
b
E
1
1
1
1
3
1
1
1
Relations r, s:
r s:
D
a
b
E
1
1
A B
a
a
C
r
s
43. Division Operation (Cont.)
Property
Let q = r s
Then q is the largest relation satisfying q x s r
Definition in terms of the basic algebra operation
Let r(R) and s(S) be relations, and let S R
r s = R-S (r ) – R-S ( ( R-S (r ) x s ) – R-S,S(r ))
To see why
R-S,S (r) simply reorders attributes of r
R-S (R-S (r ) x s ) – R-S,S(r) ) gives those tuples t in
R-S (r ) such that for some tuple u s, tu r.
44. Assignment Operation
The assignment operation () provides a convenient way to express
complex queries.
Write query as a sequential program consisting of
a series of assignments
followed by an expression whose value is displayed as a result of
the query.
Assignment must always be made to a temporary relation variable.
Example: Write r s as
temp1 R-S (r )
temp2 R-S ((temp1 x s ) – R-S,S (r ))
result = temp1 – temp2
The result to the right of the is assigned to the relation variable on
the left of the .
May use variable in subsequent expressions.
45. Bank Example Queries
Find the names of all customers who have a loan and an account at
bank.
customer_name (borrower) customer_name (depositor)
Find the name of all customers who have a loan at the bank and the
loan amount
customer_name, loan_number, amount (borrower loan)
46. Query 1
customer_name (branch_name = “Downtown” (depositor account ))
customer_name (branch_name = “Uptown” (depositor account))
Query 2
customer_name, branch_name (depositor account)
temp(branch_name) ({(“Downtown” ), (“Uptown” )})
Note that Query 2 uses a constant relation.
Bank Example Queries
Find all customers who have an account from at least the “Downtown”
and the Uptown” branches.
47. Find all customers who have an account at all branches located in
Brooklyn city.
Bank Example Queries
customer_name, branch_name (depositor account)
branch_name (branch_city = “Brooklyn” (branch))
49. Generalized Projection
Extends the projection operation by allowing arithmetic functions to be
used in the projection list.
E is any relational-algebra expression
Each of F1, F2, …, Fn are are arithmetic expressions involving constants
and attributes in the schema of E.
Given relation credit_info(customer_name, limit, credit_balance), find
how much more each person can spend:
customer_name, limit – credit_balance (credit_info)
)
(
,...,
, 2
1
E
n
F
F
F
50. Aggregate Functions and Operations
Aggregation function takes a collection of values and returns a single
value as a result.
avg: average value
min: minimum value
max: maximum value
sum: sum of values
count: number of values
Aggregate operation in relational algebra
E is any relational-algebra expression
G1, G2 …, Gn is a list of attributes on which to group (can be empty)
Each Fi is an aggregate function
Each Ai is an attribute name
)
(
)
(
,
,
(
),
(
,
,
, 2
2
1
1
2
1
E
n
n
n A
F
A
F
A
F
G
G
G
51. Aggregate Operation – Example
Relation r:
A B
C
7
7
3
10
g sum(c) (r) sum(c )
27
53. Aggregate Functions (Cont.)
Result of aggregation does not have a name
Can use rename operation to give it a name
For convenience, we permit renaming as part of aggregate
operation
branch_name g sum(balance) as sum_balance (account)
54. Outer Join
An extension of the join operation that avoids loss of information.
Computes the join and then adds tuples form one relation that does not
match tuples in the other relation to the result of the join.
Uses null values:
null signifies that the value is unknown or does not exist
All comparisons involving null are (roughly speaking) false by
definition.
We shall study precise meaning of comparisons with nulls later
55. Outer Join – Example
Relation loan
Relation borrower
customer_name loan_number
Jones
Smith
Hayes
L-170
L-230
L-155
3000
4000
1700
loan_number amount
L-170
L-230
L-260
branch_name
Downtown
Redwood
Perryridge
56. Outer Join – Example
Join
loan borrower
loan_number amount
L-170
L-230
3000
4000
customer_name
Jones
Smith
branch_name
Downtown
Redwood
Jones
Smith
null
loan_number amount
L-170
L-230
L-260
3000
4000
1700
customer_name
branch_name
Downtown
Redwood
Perryridge
Left Outer Join
loan borrower
57. Outer Join – Example
loan_number amount
L-170
L-230
L-155
3000
4000
null
customer_name
Jones
Smith
Hayes
branch_name
Downtown
Redwood
null
loan_number amount
L-170
L-230
L-260
L-155
3000
4000
1700
null
customer_name
Jones
Smith
null
Hayes
branch_name
Downtown
Redwood
Perryridge
null
Full Outer Join
loan borrower
Right Outer Join
loan borrower
58. Null Values
It is possible for tuples to have a null value, denoted by null, for some
of their attributes
null signifies an unknown value or that a value does not exist.
The result of any arithmetic expression involving null is null.
Aggregate functions simply ignore null values (as in SQL)
For duplicate elimination and grouping, null is treated like any other
value, and two nulls are assumed to be the same (as in SQL)