Relational algebra is a procedural query language used to manipulate data in relational databases. It consists of operands like relations and operators like selection, projection, union, and intersection that are applied to the operands to retrieve or construct new relations. Some common relational algebra operators are select (σ), project (π), join, union (∪), intersection (∩), and set difference. Relational algebra forms the theoretical foundation for query languages like SQL.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
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.
Query processing and Query OptimizationNiraj Gandha
This presentation on query processing and query optimization is made with many efforts. According to me, I have used the most basic/ fundamental examples and topics for the explanation.
Advance Database Management Systems -Object Oriented Principles In DatabaseSonali Parab
An OODBMS is the result of combining object oriented programming principles with database management principles. Object oriented programming concepts such as encapsulation, polymorphism and inheritance are enforced as well as database management concepts such as the ACID properties (Atomicity, Consistency, Isolation and Durability) which lead to system integrity, support for an ad hoc query language and secondary storage management systems which allow for managing very large amounts of data. The Object Oriented Database Manifesto specifically lists the following features as mandatory for a system to support before it can be called an OODBMS; Complex objects, Object identity, Encapsulation , Types and Classes , Class or Type Hierarchies, Overriding, overloading and late binding, Computational completeness , Extensibility,Persistence , Secondary storage management, Concurrency, Recovery and an Ad Hoc Query Facility.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
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.
Query processing and Query OptimizationNiraj Gandha
This presentation on query processing and query optimization is made with many efforts. According to me, I have used the most basic/ fundamental examples and topics for the explanation.
Advance Database Management Systems -Object Oriented Principles In DatabaseSonali Parab
An OODBMS is the result of combining object oriented programming principles with database management principles. Object oriented programming concepts such as encapsulation, polymorphism and inheritance are enforced as well as database management concepts such as the ACID properties (Atomicity, Consistency, Isolation and Durability) which lead to system integrity, support for an ad hoc query language and secondary storage management systems which allow for managing very large amounts of data. The Object Oriented Database Manifesto specifically lists the following features as mandatory for a system to support before it can be called an OODBMS; Complex objects, Object identity, Encapsulation , Types and Classes , Class or Type Hierarchies, Overriding, overloading and late binding, Computational completeness , Extensibility,Persistence , Secondary storage management, Concurrency, Recovery and an Ad Hoc Query Facility.
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
2. What is an “Algebra”?
Mathematical system consisting of:
Operands --- variables or values from which new values can
be constructed.
Operators --- symbols denoting procedures that construct
new values from given values.
In R1 x R2,
R1 and R2 are the operands while x is the operator . The
operator is applied on operands and new value is
constructed.
3. Relational Algebra(1970)
• It is the base of SQL
• It is procedural and formal query language .
– What to do and how to do .
• It is the collection of math-e metical expression . Not
implemented anywhere .
• It's an algebra that forms the underpinnings of
implemented languages like SQL.
7. Relational Algebra (1)
Examples: simple University admissions database
University(uName,city,enr)
Student(sID,sName,GPA,HS)
Apply(sID,uName,major,dec)
uName city enr sID sName GPA HS sID uName major dec
University Student Apply
8. Simplest query: relation name
uName city enr sID sName GPA HS sID uName major dec
University Student Apply
Relational Algebra (1)
Student
we'll get as a result a copy of the student relation
We will further use operators to filter, slice, combine
9. 9
Selection (or Restriction)
• predicate (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).
10.
11.
12.
13.
14.
15. 15
Example - Selection (or Restriction)
• List all staff with a salary greater
than £10,000.
salary > 10000 (Staff)
16. Solution
• List all staff with a salary greater than
£10,000.
• salary > 10000 (Staff)
17. Select operator (σcondition R): picks certain rows
Students with GPA>3.7
σ GPA>3.7 (Students)
Student
Relational Algebra (1)
sID sName GPA HS
1 Ahmed 3.4 1200
2 Ali 3.75 2000
sID sName GPA HS
2 Ali 3.75 2000
18. Select operator (σcondition R): picks certain rows
Students with GPA>3.7 and HS<1000
σ GPA>3.7 ^ HS<1000 (Students)
Applications to Comsats with CS as major
σ major=‘CS’ ^ uName = ‘Comsats’ (Apply)
uName city enr sID sName GPA HS sID uName major dec
University Student Apply
Relational Algebra (1)
19. 19
Projection
• col1, . . . , 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.
20.
21.
22. 22
Example - Projection
• Produce a list of salaries for all staff, showing
only staffNo, salary details.
staffNo, salary(Staff)
24. Answers
• Retrieve the roll no from table
– 𝜋 𝑟𝑜𝑙𝑙 𝑛𝑜 𝑠𝑡𝑢𝑑𝑒𝑛𝑡
• Retrieve the Name of student whose roll no is
2
– 𝜋 name (𝜎 𝑟𝑜𝑙𝑙 𝑛𝑜 = 2(𝑆𝑡𝑢𝑑𝑒𝑛𝑡 ))
25. Project operator (∏A1,A2,A3,…An R): picks certain columns
ID and major of all applications
∏sID,major (Apply)
Relational Algebra (1)
sID uName major dec
1 Comsats CS No
1 Comsats EE Yes
2 NUST CS No
Apply
sID major
1 CS
1 EE
2 CS
26. To pick both rows and columns…
ID and name of students with GPA>3.7
∏sID,sName ( σ GPA>3.7 (Students) )
Relational Algebra (1)
sID sName GPA HS
1 Ahmed 3.4 1200
2 Ali 3.75 2000
Student
sID sName
2 Ali
27. Duplicates
List of application’s majors and decisions
∏major,dec (Apply)
The semantics of relational algebra says that duplicates are always eliminated. So if you
run a query that would logically have a lot of duplicate values, you just get one value for
each result.
Relational Algebra (1)
sID uName major dec
1 Comsats CS No
1 Comsats EE Yes
2 NUST CS No
Apply
major dec
CS No
EE Yes
28.
29.
30. 30
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.
31. 31
Example - Union
• List all cities where there is either
a branch office or a property for
rent.
city(Branch)
city(PropertyForRent)
32. 32
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)
33. 33
Example - Intersection
• List all cities where there is both
a branch office and at least one
property for rent.
city(Branch)
city(PropertyForRent)
34.
35. 35
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.
36.
37.
38. Example
• Find the name of a person who is a student
but not instructor
• 𝜋 𝑛𝑎𝑚𝑒 (𝑠𝑡𝑢𝑑𝑒𝑛𝑡 )-𝜋 𝑛𝑎𝑚𝑒 (𝐼𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑜𝑟 )
39. 39
Cartesian product
• R X S
– Defines a relation that is the
concatenation of every tuple of
relation R with every tuple of
relation S.
40.
41. Cross-product (X): combine two relations
( Cartesian product)
X =
Student.sID sName GPA HS Apply.sID uName major dec
1 Ahmed 3.4 1200 1 Comsats CS No
1 Ahmed 3.4 1200 1 Comsats EE Yes
1 Ahmed 3.4 1200 2 NUST CS No
2 Ali 3.75 2000 1 Comsats CS No
2 Ali 3.75 2000 1 Comsats EE Yes
2 Ali 3.75 2000 2 NUST CS No
sID sName GPA HS
1 Ahmed 3.4 1200
2 Ali 3.75 2000
sID uName major dec
1 Comsats CS No
1 Comsats EE Yes
2 NUST CS No
Student Apply
Relational Algebra (1)
42. Cross-product (X): combine two relations
(Cartesian product)
Names and GPAs of students with HS>1000 who applied to CS
and were rejected
∏sName,GPA ( σ Student.sID=Apply.sID ^ HS>1000 ^ major=‘CS’ ^ dec=‘No’ (Students x Apply))
uName city enr sID sName GPA HS sID uName major dec
University Student Apply
Relational Algebra (1)
43. Relational Algebra (1)
Query (expression) on set of relations produces
relation as a result
Simplest query: relation name
Use operators to filter, slice, combine
Operators so far: select, project, cross-product,
natural join, theta join , rename operators
44. Names and GPAs of students with HS>1000
who applied to CS
and were rejected
Editor's Notes
For the examples in this lecture we're going to be using a simple University admission relations database with three relations. The first relation, the University relation, contains information about the University name, city, and enrollment of the University. The second relation, the student relation, contains an ID for each student, the student's name, GPA and the size of the high school they attended. And, finally, the third relation contains information about students applying to Universities. Specifically, the student's ID, the University name where they're applying, the major they're applying for and the decision of that application. I've underlined the keys for these three relations. As a reminder, a key is an attribute or a set of attributes whose value is guaranteed to be unique. So, for example, we're going to assume the University names are unique, student IDs are unique and that students will only apply to each University for a particular major one time.
The simplest query in relational algebra is a query that is simply the name of a relation. So, for example, we can write a query, "student" and that's a valid expression in relational algebra. If we run that query on our database we'll get as a result a copy of the student relation.
Now what happens next is that we're going to use operators of the relational algebra to filter relations, slice relations, and combine relations.
So, the select operator is used to pick certain rows out of a relation. The select operator is denoted by a small Sigma with a subscript--that's the condition that's used to filter the rows that we extract from the relations.
So, the select operator is used to pick certain rows out of a relation. The select operator is denoted by a small Sigma with a subscript--that's the condition that's used to filter the rows that we extract from the relations.
Our next operator is the Project Operator. So the select operator picks certain rows, and the project operator picks certain columns. So let's say we're interested in the applications, but all we wanted to know was the list of ID's and the decisions for those applications. The project operator is written using the Greek pi symbol, and now the subscript is a list of the column names that we would like to extract.
Now, what if we're interested in picking both rows and columns at the same time. So we want only some of the rows, and we want only some of the columns. Now we're going to compose operators. Remember that relational queries produce relations . So we can write a query, say, with the select operator of the students whose GPA is greater than 3.7. And now, we can take that whole expression which produces a relation, and we can apply the project operator to that, and we can get out the student ID and the student name.
And we can compose these as much as we want. We can have select over project, over select, select, project, and so on.
Now let's talk about duplicate values in the results of relational algebra queries. Let's suppose we ask for a list of the majors that people have applied for and the decision for those majors. So we write that as the project of the major and the decision on the applied relation. You might think that when we get the results of this query, we're going to have a lot of duplicate values. You can imagine in a large realistic database of applications, there's going to be hundreds of people applying for majors and having a yes or a no decision. The semantics of relational algebra says that duplicates are always eliminated. So if you run a query that would logically have a lot of duplicate values, you just get one value for each result. That's actually a bit of a difference with the SQL language. So, SQL is based on what's known as multi-sets or bags and that means that we don't eliminate duplicates, whereas relational algebra is based on sets themselves and duplicates are eliminated. There is a multi-set or bad relational algebra defined as well but we'll be fine by just considering the set relational algebra in this course.
Our first operator that combines two relations is the cross-product operator, also known as the Cartesian product. What this operator does, is it takes two relations and kinda glues them together so that their schema of the result is the combination of the schemas of the two relations and the contents of the result are every combination of tuples from those relations. So let's talk about, say, doing the cross products of students and apply. So if we do the cross product we'll get at the result a big relation, here, which is going to have eight attributes. The eight attributes across the student and apply now the only small little trick is that when we glue two relations together sometimes they'll have the same attribute and we can see we have SID on both sides. So just as a notational convention, when cross-product is done and there's two attributes that are named, they're prefaced with the name of the relation they came from. So this one would be referred to in the cross-product as the Student.SID where the other would be referred to as the Apply.SID.. Now let's talk about the contents of these. So let's suppose that the student relation had S-tuples in it and that's how many tuples, while the apply had A tuples in it, the result of the Cartesian products is gonna have S times A tuples, is going to have one tuple for every combination of tuples from the student relation and the apply relation. Now, the cross-product seems like it might not be that helpful, but what is interesting is when we use the cross-product together with other operators.
And let's see a big example of that. Let's suppose that we want to get the names and GPAs of students with a high school size greater than a thousand who applied to CS and were rejected. Okay, so let's take a look. We're going to have to access the students and the apply records in order to run this query. So what we'll do is we'll take student cross apply as our starting point. So now we have a big relation that contains eight attributes and all of those tuples that we described previously. But now we're going to start making things more interesting, because what we're going to do is a big selection over this relation. And that selection is first of all going to make sure that it only combines student and apply tuples that are referring to the same student. So to do that, we write student dot SID equals apply dot SID. So now we've filtered the result of that cross-product to only include combinations of student and apply by couples that make sets. Now we have to do a little bit of additional filtering. We said that we want the high school size to be greater than a thousand, so we do an "and" operator in the high school. We want them to have applied to CS so that's and major equals CS. We're getting a nice big query here. And finally we want them to have been rejected, so "and decision" equals, we'll just be using No for reject. So now, we've got that gigantic query. But that gets us exactly what we want except for one more thing, which is, as I said, all we want is their names and GPAs. So finally we take a big parentheses around here and we apply to that the projection operator, getting the student name and the GPA. And that is the relational algebra expression that produces the query that we have written in English.
So, in conclusion, relational algebra is a formal language. It operates on sets of relations and produces relations as a result. The simplest query is just the name of a relation and then operators are used to filter relations, slice them, and combine them. So far, we've learned the select operator for selecting rows; the project operator for selecting columns; the cross-product operator for combining every possible pair of tuples from two relations; and then two abbreviations, the natural join, which a very useful way to combine relations by enforcing a equality on certain columns; and the theta join operator.