● 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 Database Management Systemcpjcollege
Database Management System (DMBS)
• Collection of interrelated data • Set of programs to access the data • DMBS contains information about a particular enterprise • DBMS provides an environment that it both convenient and efficient to use
● 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 Database Management Systemcpjcollege
Database Management System (DMBS)
• Collection of interrelated data • Set of programs to access the data • DMBS contains information about a particular enterprise • DBMS provides an environment that it both convenient and efficient to use
DBMS - Database Management System, Introduction, Data and Database, DBMS meaning, Why DBMS?, History of DBMS, Characteristics of DBMS, Types of DBMS- Hierarchical DBMS, Network DBMS, Relational DBMS, Object-oriented DBMS, Applications of DBMS, Popular DBMS Software, Advantages of DBMS, disadvantages of DBMS.
DBMS - Database Management System, Introduction, Data and Database, DBMS meaning, Why DBMS?, History of DBMS, Characteristics of DBMS, Types of DBMS- Hierarchical DBMS, Network DBMS, Relational DBMS, Object-oriented DBMS, Applications of DBMS, Popular DBMS Software, Advantages of DBMS, disadvantages of DBMS.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
2. Outline
Data and Information
DB and DBMS
Applications of DBMS
File System
Level of abstraction
Database Language
Database Design
Data Models
Relational Databases
Database Design
Storage Manager
Query Processing
Transaction Manager
3. Data & Information
Data: It is raw, unorganized facts that need to be processed.
Data can be something simple and seemingly random and
useless until it is organized.
Example: Each student's test score is one piece of data.
Information: When data is processed, organized, structured
or presented in a given context so as to make it useful, it is
called information.
Example: The average score of a class or of the entire school
is information that can be derived from the given data.
4. Database Management System
(DBMS)
Database (DB): The collection of data, usually referred to as the
database, contains information relevant to an enterprise
Database Management System (DBMS): A database-management
system (DBMS) is a collection of interrelated data and a set of
programs to access those data.
DBMS contains information about a particular enterprise.
– Collection of interrelated data
– Set of programs to access the data
– An environment that is both convenient and efficient to use
5. Database Management System
(DBMS)
Database Applications:
Banking: transactions
Airlines: reservations, schedules
Universities: registration, grades
Sales: customers, products, purchases
Online retailers: order tracking, customized recommendations
Manufacturing: production, inventory, orders, supply chain
Human resources: employee records, salaries, tax deductions
Databases can be very large.
Databases touch all aspects of our lives
6. University Database Example
Application program examples
Add new students, instructors, and courses
Register students for courses, and generate class rosters
Assign grades to students, compute grade point averages (GPA)
and generate transcripts
7. Drawbacks of using file systems
to store data
Data redundancy and inconsistency
Multiple file formats, duplication of information in different files
Difficulty in accessing data
Need to write a new program to carry out each new task
Data isolation
Multiple files and formats
Integrity problems
Integrity constraints (e.g., account balance > 0) become “buried” in
program code rather than being stated explicitly
Hard to add new constraints or change existing ones
8. Drawbacks of using file systems to
store data (Cont.)
Atomicity of updates
Failures may leave database in an inconsistent state with partial
updates carried out
Example: Transfer of funds from one account to another should either
complete or not happen at all
Concurrent access by multiple users
Concurrent access needed for performance
Uncontrolled concurrent accesses can lead to inconsistencies
Example: Two people reading a balance (say 100) and updating it by
withdrawing money (say 50 each) at the same time
Security problems
Hard to provide user access to some, but not all, data
Database systems offer solutions to all the above problems
9. Levels of Abstraction
Physical level: describes how a record (e.g., instructor) is stored.
Logical level: describes data stored in database, and the
relationships among the data.
type instructor = record
ID : string;
name : string;
dept_name : string;
salary : integer;
end;
View level: application programs hide details of data types.
Views can also hide information (such as an employee’s salary)
for security purposes.
11. Instances and Schemas
Similar to types and variables in programming languages
Logical Schema – the overall logical structure of the database
Example: The database consists of information about a set of customers
and accounts in a bank and the relationship between them
Analogous to type information of a variable in a program
Physical schema– the overall physical structure of the database
Instance – the actual content of the database at a particular point in
time
Analogous to the value of a variable
Physical Data Independence – the ability to modify the physical
schema without changing the logical schema
Applications depend on the logical schema
In general, the interfaces between the various levels and components
should be well defined so that changes in some parts do not seriously
influence others.
12. Data Models
A collection of tools for describing
Data
Data relationships
Data semantics
Data constraints
Relational model
Entity-Relationship data model (mainly for database design)
Object-based data models (Object-oriented and Object-
relational)
Semistructured data model (XML)
Other older models:
Network model
Hierarchical model
13. Relational Model
All the data is stored in various tables.
Example of tabular data in the relational model
Columns
Rows
15. Data Definition Language (DDL)
Specification notation for defining the database schema
Example: create table instructor (
ID char(5),
name varchar(20),
dept_name varchar(20),
salary numeric(8,2))
DDL compiler generates a set of table templates stored in a data
dictionary
Data dictionary contains metadata (i.e., data about data)
Database schema
Integrity constraints
Primary key (ID uniquely identifies instructors)
Authorization
Who can access what
16. Data Manipulation Language
(DML)
Language for accessing and manipulating the data
organized by the appropriate data model
DML also known as query language
Two classes of languages
Pure – used for proving properties about computational
power and for optimization
Relational Algebra
Tuple relational calculus
Domain relational calculus
Commercial – used in commercial systems
SQL is the most widely used commercial language
17. SQL
The most widely used commercial language
SQL is NOT a Turing machine equivalent language
SQL is NOT a Turing machine equivalent language
To be able to compute complex functions SQL is usually
embedded in some higher-level language
Application programs generally access databases through
one of
Language extensions to allow embedded SQL
Application program interface (e.g., ODBC/JDBC) which allow
SQL queries to be sent to a database
18. Database Design
Logical Design – Deciding on the database schema.
Database design requires that we find a “good” collection
of relation schemas.
Business decision – What attributes should we record in the
database?
Computer Science decision – What relation schemas should
we have and how should the attributes be distributed
among the various relation schemas?
Physical Design – Deciding on the physical layout of the
database
The process of designing the general structure of the database:
20. Design Approaches
Need to come up with a methodology to ensure that each
of the relations in the database is “good”
Two ways of doing so:
Entity Relationship Model (Chapter 7)
Models an enterprise as a collection of entities and relationships
Represented diagrammatically by an entity-relationship diagram:
Normalization Theory (Chapter 8)
Formalize what designs are bad, and test for them
21. Object-Relational Data Models
Relational model: flat, “atomic” values
Object Relational Data Models
Extend the relational data model by including object orientation
and constructs to deal with added data types.
Allow attributes of tuples to have complex types, including non-
atomic values such as nested relations.
Preserve relational foundations, in particular the declarative access
to data, while extending modeling power.
Provide upward compatibility with existing relational languages.
22. XML: Extensible Markup
Language
Defined by the WWW Consortium (W3C)
Originally intended as a document markup language not a
database language
The ability to specify new tags, and to create nested tag
structures made XML a great way to exchange data, not just
documents
XML has become the basis for all new generation data
interchange formats.
A wide variety of tools is available for parsing, browsing and
querying XML documents/data
24. Storage Management
Storage manager is a program module that provides the
interface between the low-level data stored in the database
and the application programs and queries submitted to the
system.
The storage manager is responsible to the following tasks:
Interaction with the OS file manager
Efficient storing, retrieving and updating of data
Issues:
Storage access
File organization
Indexing and hashing
26. Query Processing (Cont.)
Alternative ways of evaluating a given query
Equivalent expressions
Different algorithms for each operation
Cost difference between a good and a bad way of
evaluating a query can be enormous
Need to estimate the cost of operations
Depends critically on statistical information about relations
which the database must maintain
Need to estimate statistics for intermediate results to compute
cost of complex expressions
27. Transaction Management
What if the system fails?
What if more than one user is concurrently updating the
same data?
A transaction is a collection of operations that performs a
single logical function in a database application
Transaction-management component ensures that the
database remains in a consistent (correct) state despite
system failures (e.g., power failures and operating system
crashes) and transaction failures.
Concurrency-control manager controls the interaction
among the concurrent transactions, to ensure the
consistency of the database.
30. Database Architecture
The architecture of a database systems is greatly influenced by
the underlying computer system on which the database is
running:
Centralized
Client-server
Parallel (multi-processor)
Distributed
31. History of Database Systems
1950s and early 1960s:
Data processing using magnetic tapes for storage
Tapes provided only sequential access
Punched cards for input
Late 1960s and 1970s:
Hard disks allowed 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
32. 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
Early 2000s:
XML and XQuery standards
Automated database administration
Later 2000s:
Giant data storage systems
Google BigTable, Yahoo PNuts, Amazon, ..