This document discusses different SQL commands covered in a Relational Database Management Systems course taught by Prof. Krishna Roy. It describes Data Definition Language commands (CREATE, ALTER, DROP, TRUNCATE) used to define and modify database structures. It also covers Data Manipulation Language commands (INSERT, UPDATE, DELETE) used to manage the data within these structures. Transaction Control Language and Data Query Language commands are also briefly introduced.
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.
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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.
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
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
MIS 301 RELATIONAL DATABASE MANAGEMENT SYSTEM 10,11&12.pptx
1. MIS 301 RELATIONAL DATABASE
MANAGEMENT SYSTEM
DATABASE MANAGEMENT SYSTEM
Structured Query Language(SQL)-2
LECTURE 10,11,12
PROF. KRISHNA ROY
2. Types of SQL Commands
•DDL or Data Definition Language Command
• DML or Data Manipulation Language Command
•DCL or Data Control Language Command
• TCL or Transaction Control Language Command
• DQL or Data Query Language Command
PROF. KRISHNA ROY, FMS, BCREC 2
20-09-2023
3. Data definition language
• DDL is used for creating table structures, modifying existing
structures as well as for removing such structures
• DDL commands are auto-committed i.e. changes made by these
commands are permanently saved in the database.
• DDL commands are CREATE, ALTER, DROP, TRUNCATE
PROF. KRISHNA ROY, FMS, BCREC 3
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4. ddl command create
• This command creates a new table in the database.
• Syntax:
create table <table name>
( attrib1 datatype(size),
attrib2 datatype(size),
: :
attribn datatype(size));
• Example:
create table student
( roll_no char(10),
name char(20)
dt_of_birth date,
stream char(10)
city char(10),
marks number(5,1));
PROF. KRISHNA ROY, FMS, BCREC 4
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5. ddl command create
The following constraints are commonly used with the CREATE TABLE command
• NOT NULL - Ensures that a column cannot have a NULL value
• UNIQUE - Ensures that all values in a column are different
• PRIMARY KEY - A combination of a NOT NULL and UNIQUE. Uniquely identifies each row
in a table
• FOREIGN KEY - Uniquely identifies a row/record in another table
• CHECK - Ensures that all values in a column satisfies a specific condition
• DEFAULT - Sets a default value for a column when no value is specified
Example:
create table student
( roll_no char(10) not null primary key,
name char(20) not null,
dt_of_birth date,
mob_no number(10) unique,
stream char(10) check (stream in (‘Marketing’,’MIS’,’Finance’,’HR’)
city_code char(10) foreign key references city(city_code) default ‘Durgapur’,
marks number(5,1));
PROF. KRISHNA ROY, FMS, BCREC 5
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6. DDl command alter
• Alter command is used for changing the table structure.
• Alter command with the add clause adds new attribute definitions to an
existing table.
• Syntax with add clause
alter table <table name>
add ( attrib1 datatype(size),
: :
attribn datatype(size));
• Example
alter table student
add ( fname char(20),
result bool);
PROF. KRISHNA ROY, FMS, BCREC 6
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7. DDl command alter
• Alter command is used for changing the table structure.
• Alter command with the modify clause changes the type and size of existing
attributes.
• Syntax with modify clause
alter table <table name>
modify ( attrib1 datatype(size),
: :
attribn datatype(size));
• Example
alter table student
modify( roll_no number(10),
name char(30));
PROF. KRISHNA ROY, FMS, BCREC 7
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8. DDl command drop
• Drop command is used for removing the table structure
along with contents.
• Syntax
drop table <table name>;
• Example
drop table student;
PROF. KRISHNA ROY, FMS, BCREC 8
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9. DDl command truncate
• Truncate command is used for removing the table
contents and not the table definition.
• Syntax
truncate table <table name>;
• Example
truncate table student;
PROF. KRISHNA ROY, FMS, BCREC 9
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10. Data manipulation language
• DML is used for changing contents of the database
• DML commands are not auto-committed i.e. changes made by
these commands are not permanently saved in the database and
can be rolled back.
• DML commands are INSERT, UPDATE AND DELETE.
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11. DML command insert
• The insert command is used for adding a row of data or record into a table
• There are more or less three variations of the insert command
as follows:
1. insert into <tablename>
values(val1, val2, ……, valn); here the field values must be provided
sequentially. No field value may be omitted though it may be replaced by
NULL(representing absence of a value)
Example:
insert into student values(123,’John’,’25-Mar-97’, ’marketing’, ’Durgapur’, 81.5, ’Robbins’, TRUE);
PROF. KRISHNA ROY, FMS, BCREC 11
20-09-2023
12. DML command insert
• The insert command is used for adding a row of data or record
into a table
• There are more or less three variations of the insert command
as follows:
2. insert into <tablename> (attrib1, attrib2,…, attribn)
values(val1, val2, ……, valn); here the field values must be
provided only for mentioned attributes in the mentioned sequence.
Example:
insert into student(name,marks,result,roll_no) values(‘Nikhil’, 75.0,TRUE,212);
PROF. KRISHNA ROY, FMS, BCREC 12
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13. DML command insert
• The insert command is used for adding a row of data or record into a table
• There are more or less three variations of the insert command
as follows:
3. insert into <tablename> (attrib1, attrib2,…, attribn)
values(&val1, &val2, ……, &valn); here the field values are input at runtime after
prompting and then the accepted entries are inserted as a record.
Example:
insert into student(name,marks,result,roll_no) values(‘&name’, &marks, &result, &roll_no);
Enter value for name: Girish
Enter value for marks: 25.5
Enter value for result: FALSE
Enter value for roll_no: 102
Every time this command(macro substitution) is repeated, a new record can be input by the user with new values. The first
2 formats of insert, if repeated, insert duplicate records.
PROF. KRISHNA ROY, FMS, BCREC 13
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14. DML command update
• The update command is used for modifying/changing the content of a table
• In absence of the where clause , update command updates all the records
• The set clause is used to assign a constant value or an expression(after evaluation)
to an attribute in a tuple.
• Syntax: update <table name> set attrib1=value/expr, attrib2=value/expr, ……;
• Example: update student set stream=‘Finance’, marks=marks+10;
Stream for all students are set to Finance irrespective of their previous stream and
everyone’s marks are increased by 10
• In presence of the where clause, only the records satisfying the where clause are updated.
• Syntax: update <table name> set attrib1=value/expr, attrib2=value/expr, … where condition;
• Example: update student set stream=‘Finance’, marks=marks+10 where city=‘Durgapur’ and marks<33;
Stream for students residing in Durgapur and having obtained less than 33 marks are set to
Finance irrespective of their previous stream and their marks are increased by 10
PROF. KRISHNA ROY, FMS, BCREC 14
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15. DML command delete
• The delete command is used for removing entire records/tuples from a table
• In absence of the where clause , delete removes all the records from the table.
• If where clause is used then the records satisfying the where clause only, are
deleted.
• Syntax: delete from <table name>;
• Example: delete from student;
All student records are deleted leaving the empty table structure/definition.
• In presence of the where clause, only the records satisfying the where clause are updated.
• Syntax: delete from <table name> where condition;
• Example: delete from student where marks<33;
Records of students securing less than 33 are deleted. Rest of the records remain intact.
• delete either deletes entire records or not at all . Part of the record i.e. some of the
attributes can not be deleted using this command
PROF. KRISHNA ROY, FMS, BCREC 15
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16. • Till we meet again in the next class……….
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