This week's session is on SQL Views: what they are, how to create them, how to insert, update and delete data through them along with other key details to know!
Watch the video at:
http://www.aaronbuma.com/2016/01/views/
This week's session is on SQL Views: what they are, how to create them, how to insert, update and delete data through them along with other key details to know!
Watch the video at:
http://www.aaronbuma.com/2016/01/views/
in this presentation the commands let you help to understand the basic of the database system software. how to retrieve data, how to feed data and manipulate it very efficiently by using this commands.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
Aggregate functions are functions that take a collection of values as input and return a single value.The ISO standard defines five (5) aggregate functions namely :-
1) COUNT
2) SUM
3) AVG
4) MIN
5) MAX
1.COUNT Function
The COUNT function returns the total number of values in the specified field. It works on both numeric and non-numeric data types. All aggregate functions by default exclude nulls values before working on the data.
MIN function
The MIN function returns the smallest value in the specified table field.
2.MAX function
Just as the name suggests, the MAX function is the opposite of the MIN function. It returns the largest value from the specified table field.
3.SUM function
Suppose we want a report that gives total amount of payments made so far. We can use the MySQL SUM function which returns the sum of all the values in the specified column. SUM works on numeric fields only. Null values are excluded from the result returned.
4.AVG function
MySQL AVG function returns the average of the values in a specified column. Just like the SUM function, it works only on numeric data types.
5.MIN function
The MIN function returns the smallest value in the specified table field.
Data Definition Language (DDL), Data Definition Language (DDL), Data Manipulation Language (DML) , Transaction Control Language (TCL) , Data Control Language (DCL) - , SQL Constraints
in this presentation the commands let you help to understand the basic of the database system software. how to retrieve data, how to feed data and manipulate it very efficiently by using this commands.
Consists of the explanations of the basics of SQL and commands of SQL.Helpful for II PU NCERT students and also degree studeents to understand some basic things.
Aggregate functions are functions that take a collection of values as input and return a single value.The ISO standard defines five (5) aggregate functions namely :-
1) COUNT
2) SUM
3) AVG
4) MIN
5) MAX
1.COUNT Function
The COUNT function returns the total number of values in the specified field. It works on both numeric and non-numeric data types. All aggregate functions by default exclude nulls values before working on the data.
MIN function
The MIN function returns the smallest value in the specified table field.
2.MAX function
Just as the name suggests, the MAX function is the opposite of the MIN function. It returns the largest value from the specified table field.
3.SUM function
Suppose we want a report that gives total amount of payments made so far. We can use the MySQL SUM function which returns the sum of all the values in the specified column. SUM works on numeric fields only. Null values are excluded from the result returned.
4.AVG function
MySQL AVG function returns the average of the values in a specified column. Just like the SUM function, it works only on numeric data types.
5.MIN function
The MIN function returns the smallest value in the specified table field.
Data Definition Language (DDL), Data Definition Language (DDL), Data Manipulation Language (DML) , Transaction Control Language (TCL) , Data Control Language (DCL) - , SQL Constraints
SQL is a database computer language designed for the retrieval and management of data in relational database. SQL stands for Structured Query Language.
SQL Developer provides powerful editors for working with SQL, PL/SQL, Stored Java Procedures, and XML.
Run queries, generate execution plans, export data to the desired format (XML, Excel, HTML, PDF, etc.), execute, debug, test, and document your database programs, and much more with SQL Developer.
SQL Developer provides powerful editors for working with SQL, PL/SQL, Stored Java Procedures, and XML.
Database Management System Practical File. DBMS is a software that manages the databases. It is an important subject in computer science (CSE). In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. A database management system is the software that interacts with end users, applications, and the database itself to capture and analyze the data. he DBMS software additionally encompasses the core facilities provided to administer the database. The sum total of the database, the DBMS and the associated applications can be referred to as a database system
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Cosmetic shop management system project report.pdfKamal Acharya
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.
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.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
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.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
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.
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.
3. Difference between DBMS and RDBMS
S.N DBMS TDBMS
1
Introduced in 1960s. Introduced in 1970s.
2 During introduction it followed the
navigational modes (Navigational
DBMS) for data storage and
fetching.
This model uses relationship
between tables using primary keys,
foreign keys .
3 Data fetching is slower for
complex and large amount of data.
Comparatively faster because of its
relational model.
4 Used for applications using small
amount of data.
Used for complex and large amount
of data.
5 Data Redundancy is common in
this model
Keys and indexes are used in the
tables to avoid redundancy.
6 Example systems are dBase,,
LibreOffice Base, FoxPro.
Example systems are SQL Server,
Oracle ,MS ACESS, MySQL,
MariaDB, SQLite.
4. The ANSI standard language for the definition and
manipulation of relational database.
Includes data definition language (DDL), statements that
specify and modify database schemas.
Includes a data manipulation language (DML), statements
that manipulate database content.
Structured Query Language (SQL)
5. Some Facts on SQL
SQL data is case-sensitive, SQL commands are not.
First Version was developed at IBM by Donald D.
Chamberlin and Raymond F. Boyce. [SQL]
Developed using Dr. E.F. Codd's paper, “A Relational Model
of Data for Large Shared Data Banks.”
SQL query includes references to tuples variables and the
attributes of those variables
6. SQL: DDL Commands
CREATE TABLE: used to create a table.
ALTER TABLE: modifies a table after it was created.
DROP TABLE: removes a table from a database.
7. SQL: CREATE TABLE Statement
Things to consider before you create your table are:
The type of data
the table name
what column(s) will make up the primary key
the names of the columns
CREATE TABLE statement syntax:
CREATE TABLE <table name>
( field1 datatype ( NOT NULL ),
field2 datatype ( NOT NULL )
);
9. SQL: ALTER TABLE Statement
To add or drop columns on existing tables.
ALTER TABLE statement syntax:
ALTER TABLE <table name>
ADD attr datatype;
or
DROP COLUMN attr;
10. SQL: DROP TABLE Statement
DROP TABLE statement syntax:
DROP TABLE <table name> ;
11. Example:
CREATE TABLE FoodCart (
date varchar(10),
food varchar(20),
profit float
);
ALTER TABLE FoodCart (
ADD sold int
);
ALTER TABLE FoodCart(
DROP COLUMN profit
);
DROP TABLE FoodCart;
profit
food
date
sold
profit
food
date
sold
food
date
FoodCart
FoodCart
FoodCart
12. SQL: DML Commands
INSERT: adds new rows to a table.
UPDATE: modifies one or more attributes.
DELETE: deletes one or more rows from a table.
13. SQL: INSERT Statement
To insert a row into a table, it is necessary to have a value
for each attribute, and order matters.
INSERT statement syntax:
INSERT into <table name>
VALUES ('value1', 'value2', NULL);
Example: INSERT into FoodCart
VALUES (’02/26/08', ‘pizza', 70 );
FoodCart
70
pizza
02/26/08
500
hotdog
02/26/08
350
pizza
02/25/08
sold
food
date
500
hotdog
02/26/08
350
pizza
02/25/08
sold
food
date
14. SQL: UPDATE Statement
To update the content of the table:
UPDATE statement syntax:
UPDATE <table name> SET <attr> = <value>
WHERE <selection condition>;
Example: UPDATE FoodCart SET sold = 349
WHERE date = ’02/25/08’AND food = ‘pizza’;
FoodCart
70
pizza
02/26/08
500
hotdog
02/26/08
350
pizza
02/25/08
sold
food
date
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
15. SQL: DELETE Statement
To delete rows from the table:
DELETE statement syntax:
DELETE FROM <table name>
WHERE <condition>;
Example: DELETE FROM FoodCart
WHERE food = ‘hotdog’;
FoodCart
Note: If the WHERE clause is omitted all rows of data are deleted from the table.
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
70
pizza
02/26/08
349
pizza
02/25/08
sold
food
date
16. SQL Statements, Operations, Clauses
SQL Statements:
Select
SQL Operations:
Join
Left Join
Right Join
Like
SQL Clauses:
Order By
Group By
Having
17. SQL: SELECT Statement
A basic SELECT statement includes 3 clauses
SELECT <attribute name> FROM <tables> WHERE <condition>
SELECT
Specifies the attributes
that are part of the
resulting relation
FROM
Specifies the tables
that serve as the input
to the statement
WHERE
Specifies the selection
condition, including
the join condition.
18. Using a “*” in a select statement indicates that every
attribute of the input table is to be selected.
Example: SELECT * FROM … WHERE …;
To get unique rows, type the keyword DISTINCT after
SELECT.
Example: SELECT DISTINCT * FROM …
WHERE …;
SQL: SELECT Statement (cont.)
20. SQL: Join operation
A join can be specified in the FROM clause which
list the two input relations and the WHERE clause
which lists the join condition.
Example:
Biotech
1003
Sales
1002
IT
1001
Division
ID
TN
1002
MA
1001
CA
1000
State
ID
Emp Dept
21. SQL: Join operation (cont.)
Sales
1002
IT
1001
Dept.Division
Dept.ID
TN
1002
MA
1001
Emp.State
Emp.ID
inner join = join
SELECT *
FROM emp join dept (or FROM emp, dept)
on emp.id = dept.id;
22. SQL: Join operation (cont.)
IT
1001
Sales
1002
null
null
Dept.Division
Dept.ID
CA
1000
TN
1002
MA
1001
Emp.State
Emp.ID
left outer join = left join
SELECT *
FROM emp left join dept
on emp.id = dept.id;
23. SQL: Join operation (cont.)
Sales
1002
Biotech
1003
IT
1001
Dept.Division
Dept.ID
MA
1001
null
null
TN
1002
Emp.State
Emp.ID
right outer join = right join
SELECT *
FROM emp right join dept
on emp.id = dept.id;
24. SQL: Like operation
Pattern matching selection
% (arbitrary string)
SELECT *
FROM emp
WHERE ID like ‘%01’;
finds ID that ends with 01, e.g. 1001, 2001, etc
_ (a single character)
SELECT *
FROM emp
WHERE ID like ‘_01_’;
finds ID that has the second and third character as 01, e.g.
1010, 1011, 1012, 1013, etc
25. SQL: The ORDER BY Clause
Ordered result selection
desc (descending order)
SELECT *
FROM emp
order by state desc
puts state in descending order, e.g. TN, MA, CA
asc (ascending order)
SELECT *
FROM emp
order by id asc
puts ID in ascending order, e.g. 1001, 1002, 1003
26. SQL: The GROUP BY Clause
The function to divide the tuples into groups and returns an
aggregate for each group.
Usually, it is an aggregate function’s companion
SELECT food, sum(sold) as totalSold
FROM FoodCart
group by food;
FoodCart
419
pizza
500
hotdog
totalSold
food
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
27. SQL: The HAVING Clause
The substitute of WHERE for aggregate functions
Usually, it is an aggregate function’s companion
SELECT food, sum(sold) as totalSold
FROM FoodCart
group by food
having sum(sold) > 450;
FoodCart
500
hotdog
totalSold
food
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
28. SQL: Aggregate Functions
Are used to provide summarization information for SQL
statements, which return a single value.
COUNT(attr)
SUM(attr)
MAX(attr)
MIN(attr)
AVG(attr)
Note: when using aggregate functions, NULL values are not
considered, except in COUNT(*) .
29. SQL: Aggregate Functions (cont.)
COUNT(attr) -> return # of rows that are not null
Ex: COUNT(distinct food) from FoodCart; -> 2
SUM(attr) -> return the sum of values in the attr
Ex: SUM(sold) from FoodCart; -> 919
MAX(attr) -> return the highest value from the attr
Ex: MAX(sold) from FoodCart; -> 500
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
FoodCart
30. SQL: Aggregate Functions (cont.)
MIN(attr) -> return the lowest value from the attr
Ex: MIN(sold) from FoodCart; -> 70
AVG(attr) -> return the average value from the attr
Ex: AVG(sold) from FoodCart; -> 306.33
Note: value is rounded to the precision of the datatype
70
pizza
02/26/08
500
hotdog
02/26/08
349
pizza
02/25/08
sold
food
date
FoodCart