The relational database model derived from the mathematical concept of relation and set theory. It was proposed as a technique to data modeling by Dr Edgar F. Codd of IBM Analysis in 1970 in his document entitled “A Relational Technique of Information for Huge Shared Data Banks.” This document marked the start of the field of a relational database.
https://www.ducatindia.com/datascienceusingpython
1. What is Entity Relationship Model
2. Entity and Entity Set
3. Relationship and Relationship Set
4. Attributes and it's kinds
5. Participation Constraints and Mapping Cardinality
6. Aggregation, Specialization, and Generalization
7. Some Sample ERD models
The relational database model derived from the mathematical concept of relation and set theory. It was proposed as a technique to data modeling by Dr Edgar F. Codd of IBM Analysis in 1970 in his document entitled “A Relational Technique of Information for Huge Shared Data Banks.” This document marked the start of the field of a relational database.
https://www.ducatindia.com/datascienceusingpython
1. What is Entity Relationship Model
2. Entity and Entity Set
3. Relationship and Relationship Set
4. Attributes and it's kinds
5. Participation Constraints and Mapping Cardinality
6. Aggregation, Specialization, and Generalization
7. Some Sample ERD models
The ppt will give the information about the ER-model.
and also here I added one ER-diagram of Placement Management System which will help anyone to understand the components of ER-diagram.
The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
The ppt will give the information about the ER-model.
and also here I added one ER-diagram of Placement Management System which will help anyone to understand the components of ER-diagram.
The Relational Data Model and Relational Database Constraints Ch5 (Navathe 4t...Raj vardhan
The Relational Data Model and Relational Database Constraints
Ch5 (Navathe 4th edition)/ Ch7 (Navathe 3rd edition)
Example of STUDENT Relation(figure 5.1)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. Relational database schema
Relational database usually contains many relations , with
tuples in relations that are related in various ways.
Relational schema S={R1,R2,R3,…….Rm}
Eg. COMPANY={EMPLOYEE,DEPARTMENT,
DEPT_LOCATIONS,PROJECT,WORKS_ON,
DEPENDENT}
6. Entity integrity constraint
No primary key value can be NULL
Used to identify the individual tuple in a relation
If two or more tuples values had NULL for their primary
keys we might not able to diffrentiate.
7. Referential integrity constraints
Is specified between two relations
To maintain the consistency among tuples in the two
relations ives
Eg. Dno of EMPLOYEE and Dnumber of
DEPARTMENT
8. Foreign key
a set of attributes FK in relation schema R1 is a foreign
key of R1 that reference relation R2
Conditions
i. The attribute in FK have the same domain as the
primary key attribute of R2
ii. t1[FK]=t2[PK]
Eg. Dno of EMPLOYEE and Dnumber of
DEPARTMENT
