The document discusses database systems and Entity-Relationship (E-R) modeling. It defines various symbols and concepts in E-R diagramming such as entity types, attributes, relationships and cardinalities. It also outlines the process of designing a database schema including requirements analysis, conceptual design, logical design and physical design. Specialization, generalization and other enhanced E-R modeling features are presented with examples. Finally, it provides a brief history of database systems from the 1950s to the present.
An Introduction To Software Development - Architecture & Detailed DesignBlue Elephant Consulting
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://youtu.be/PXYATve92zU
Performance Optimization of Recommendation Training Pipeline at Netflix DB Ts...Databricks
Netflix is the world’s largest streaming service, with over 80 million members worldwide. Machine learning algorithms are used to recommend relevant titles to users based on their tastes.
At Netflix, we use Apache Spark to power our recommendation pipeline. Stages in the pipeline, such as label generation, data retrieval, feature generation, training, validation, are based on Spark ML PipleStage framework. While this provides developers the flexibility to develop individual components as encapsulated pipeline stages, we find that coordination across stages can potentially provide significant performance gains.
In this talk, we discuss how our machine learning pipeline based on Spark has been improved over the years. Techniques such as predicate pushdown, wide transformation minimization, have lead to significant run time improvement and resource savings.
To make this comparison we need to first consider the problem that both approaches help us to solve. When programming any system you are essentially dealing with data and the code that changes that data. These two fundamental aspects of programming are handled quite differently in procedural systems compared with object oriented systems, and these differences require different strategies in how we think about writing code.
An Introduction To Software Development - Architecture & Detailed DesignBlue Elephant Consulting
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
https://youtu.be/PXYATve92zU
Performance Optimization of Recommendation Training Pipeline at Netflix DB Ts...Databricks
Netflix is the world’s largest streaming service, with over 80 million members worldwide. Machine learning algorithms are used to recommend relevant titles to users based on their tastes.
At Netflix, we use Apache Spark to power our recommendation pipeline. Stages in the pipeline, such as label generation, data retrieval, feature generation, training, validation, are based on Spark ML PipleStage framework. While this provides developers the flexibility to develop individual components as encapsulated pipeline stages, we find that coordination across stages can potentially provide significant performance gains.
In this talk, we discuss how our machine learning pipeline based on Spark has been improved over the years. Techniques such as predicate pushdown, wide transformation minimization, have lead to significant run time improvement and resource savings.
To make this comparison we need to first consider the problem that both approaches help us to solve. When programming any system you are essentially dealing with data and the code that changes that data. These two fundamental aspects of programming are handled quite differently in procedural systems compared with object oriented systems, and these differences require different strategies in how we think about writing code.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. Subject Name Code Credit Hours
Database System COMP 219 3
E-R Diagram
• Symbol Description
Entity Type
Attribute
Key Attribute
3. Subject Name Code Credit Hours
Database System COMP 219 3
E-R Diagram
Symbol Description
Composite Attribute
Multivalued attribute
Attribute
4. Subject Name Code Credit Hours
Database System COMP 219 3
E-R Diagram
Symbol Description
Derived Attribute
Relationship
5. Subject Name Code Credit Hours
Database System COMP 219 3
E-R Diagram
• Symbol Description
Identifying Relationship
Weak Entity Type
6. Subject Name Code Credit Hours
Database System COMP 219 3
E-R Diagram
• Symbol Description
E1 E2R
Total participation of E2 in R &
Partial Participation of E1 in R
E1 E2R
1 1
Cardinality Ratio
E2R
Min,max
Structural constraints
(min,max) on participation of
Environmental in R
7. Subject Name Code Credit Hours
Database System COMP 219 3
Recursive Relationship
• If the same entity type partcipates in a
relationship more than once in different roles.
• E.g.. Employee
Supervising
Supervisor
Supervise
8. Subject Name Code Credit Hours
Database System COMP 219 3
Design of an E-R Database Schema
The steps involved in designing an E-R database schema are,
• Identify entity types and their entity sets.
• List out the attributes of each entity type.
• Relate several entities by specifyiing some relationship that
exists among them.
• Specify some attributes of relation if any.
• Specify Generalization and specialization any exists.
• Specify Aggregation (global) if any used.
9. Subject Name Code Credit Hours
Database System COMP 219 3
Design Process:
• The main phases involved in designing a ER db schema is shown below,
Mini world
Requirements collection & Analysis
Data Requirements
Conceptual Design
Conceptual schema
Logical Design
Physical design
Logical schema
Internal Schema
Transaction
implementation
Functional Requirements
Functional Analysis
High level Transaction
specification
Application program
Design
App.pgms
DBMS
Independen
t
DBMS
Specific
10. Subject Name Code Credit Hours
Database System COMP 219 3
Requirements collection & Analysis
• The db designers interview db users to understand & document their
requirements.
• They find out data requirements (what data are stored in the db).
Conceptual Design:
• Once the requirements are documented , the next step is to create
conceptual schema which carried out in conceptual design Phase.
• It describes the structure of a db in the form of entity type, relationship
among them & constraints.
11. Subject Name Code Credit Hours
Database System COMP 219 3
Logical Design
• The actual implementation of the db is carried out using DBMS.
Physical Design
The last phase is the internal storage structures, indexes,
access paths, and file organizations for the db files are
specified.
In parallel with these activities, Application programs are
designed and implemented as db transactions.
12. Subject Name Code Credit Hours
Database System COMP 219 3
EER Model- Enhanced or Extended E-R model
• Using E-R model only the basic features of a db.
• Some enhanced features such as Specialization, Generalization, Union &
aggregation can be shown using EER model.
A. SPECIALIZATION:
The process of designating sub grouping within an
entity set..
13. Subject Name Code Credit Hours
Database System COMP 219 3
E.g…..
Employee
IS A
Secretary Technician Manager
eid ename eaddr Job
Typing speed
Mgrid
14. Subject Name Code Credit Hours
Database System COMP 219 3It is also represented as
Employee
eid ename eaddr Job
d
Secretary Technician Manager
d
Job Type
Salary Type
Hourly Regular
Defining attribute
Sub classes
15. Subject Name Code Credit Hours
Database System COMP 219 3
Generalization
•The process of defining a
generalized entity type
from the given entity types.
16. Subject Name Code Credit Hours
Database System COMP 219 3E.g…
CAR TRUCK
PriceMax speed
Vehicle ID No. of seats Vehicle ID
Price
No. Of Axles
Tonnage
17. Subject Name Code Credit Hours
Database System COMP 219 3
Vehicle
Vehicle ID
Price
d
CAR
TRUCK
Max speed
No. of seats Tonnage
No. Of Axles
18. Subject Name Code Credit Hours
Database System COMP 219 3
History of Database Systems
• 1950s and early 1960s:
– Data processing using magnetic tapes for storage
• Tapes provide only sequential access
– Punched cards for input
• Late 1960s and 1970s:
– Hard disks allow direct access to data
– Network and hierarchical data models in widespread use
– Ted Codd defines the relational data model
• Would win the ACM Turing Award for this work
• IBM Research begins System R prototype
• UC Berkeley begins Ingres prototype
– High-performance (for the era) transaction processing
19. Subject Name Code Credit Hours
Database System COMP 219 3
History (cont.)
• 1980s:
– Research relational prototypes evolve into commercial systems
• SQL becomes industrial standard
– Parallel and distributed database systems
– Object-oriented database systems
• 1990s:
– Large decision support and data-mining applications
– Large multi-terabyte data warehouses
– Emergence of Web commerce
• 2000s:
– XML and XQuery standards
– Automated database administration