SlideShare a Scribd company logo
[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Relational Database: Definitions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example Instance of Students Relation ,[object Object],[object Object],[object Object]
Relational Query Languages ,[object Object],[object Object],[object Object],[object Object]
The SQL Query Language SELECT  * FROM   Students S WHERE   S.age=18 ,[object Object],SELECT   S.name, S.login
Querying Multiple Relations ,[object Object],SELECT  S.name, E.cid FROM   Students S, Enrolled E WHERE   S.sid=E.sid  AND  E.grade=“A” Given the following instances of Enrolled and Students: we get:
Creating Relations in SQL ,[object Object],[object Object],CREATE TABLE Students (sid:  CHAR(20) ,    name:  CHAR(20) ,    login:  CHAR(10),   age:  INTEGER ,   gpa:  REAL )  CREATE TABLE Enrolled (sid:  CHAR(20) ,    cid:  CHAR(20) ,    grade:  CHAR (2))
Destroying and Altering Relations ,[object Object],DROP   TABLE   Students  ,[object Object],ALTER   TABLE   Students  ADD   COLUMN   firstYear: integer
Adding and Deleting Tuples ,[object Object],INSERT INTO  Students (sid, name, login, age, gpa) VALUES   (53688, ‘Smith’, ‘smith@ee’, 18, 3.2) ,[object Object],DELETE   FROM  Students S WHERE  S.name = ‘Smith’
Integrity Constraints (ICs) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primary Key Constraints ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Primary and Candidate Keys in SQL ,[object Object],CREATE TABLE Enrolled (sid CHAR(20) cid  CHAR(20), grade CHAR(2), PRIMARY KEY   (sid,cid)  ) ,[object Object],[object Object],CREATE TABLE Enrolled (sid CHAR(20) cid  CHAR(20), grade CHAR(2), PRIMARY KEY   (sid), UNIQUE  (cid, grade) )
Foreign Keys, Referential Integrity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Foreign Keys in SQL ,[object Object],CREATE TABLE Enrolled (sid CHAR(20),  cid CHAR(20),  grade CHAR(2), PRIMARY KEY   (sid,cid), FOREIGN KEY   (sid)  REFERENCES   Students ) Enrolled Students
Enforcing Referential Integrity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Referential Integrity in SQL ,[object Object],[object Object],[object Object],[object Object],CREATE TABLE  Enrolled (sid  CHAR (20), cid  CHAR(20) , grade  CHAR (2), PRIMARY KEY   (sid,cid), FOREIGN KEY   (sid) REFERENCES   Students ON DELETE CASCADE ON UPDATE SET  DEFAULT  )
Where do ICs Come From? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Logical DB Design: ER to Relational ,[object Object],CREATE TABLE  Employees  (ssn  CHAR (11), name  CHAR (20), lot  INTEGER , PRIMARY KEY   (ssn)) Employees ssn name lot
Relationship Sets to Tables ,[object Object],[object Object],[object Object],[object Object],CREATE TABLE  Works_In( ssn  CHAR (11), did  INTEGER , since  DATE , PRIMARY KEY  (ssn, did), FOREIGN KEY  (ssn)  REFERENCES  Employees, FOREIGN KEY  (did)  REFERENCES  Departments)
Review: Key Constraints ,[object Object],Translation to  relational model? Many-to-Many 1-to-1 1-to Many Many-to-1 budget did Departments dname since lot name ssn Manages Employees
Translating ER Diagrams with Key Constraints ,[object Object],[object Object],[object Object],[object Object],CREATE TABLE  Manages( ssn  CHAR(11) , did  INTEGER , since  DATE , PRIMARY KEY  (did), FOREIGN KEY  (ssn)  REFERENCES  Employees, FOREIGN KEY  (did)  REFERENCES  Departments) CREATE TABLE  Dept_Mgr( did  INTEGER, dname  CHAR(20), budget  REAL, ssn  CHAR(11) , since  DATE , PRIMARY KEY  (did), FOREIGN KEY  (ssn)  REFERENCES  Employees)
Review: Participation Constraints ,[object Object],[object Object],[object Object],lot name dname budget did since name dname budget did since Manages since Departments Employees ssn Works_In
Participation Constraints in SQL ,[object Object],CREATE TABLE  Dept_Mgr( did  INTEGER, dname  CHAR(20) , budget  REAL , ssn  CHAR(11)  NOT NULL , since  DATE , PRIMARY KEY  (did), FOREIGN KEY  (ssn)  REFERENCES  Employees, ON DELETE NO ACTION )
Review: Weak Entities ,[object Object],[object Object],[object Object],lot name age pname Dependents Employees ssn Policy cost
Translating Weak Entity Sets ,[object Object],[object Object],CREATE TABLE  Dep_Policy ( pname  CHAR(20) , age  INTEGER , cost  REAL , ssn  CHAR(11) NOT NULL , PRIMARY KEY  (pname, ssn), FOREIGN KEY  (ssn)  REFERENCES  Employees, ON DELETE CASCADE )
Review: ISA Hierarchies ,[object Object],[object Object],Contract_Emps name ssn Employees lot hourly_wages ISA Hourly_Emps contractid hours_worked ,[object Object],[object Object]
Translating ISA Hierarchies to Relations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Review: Binary vs. Ternary Relationships ,[object Object],age pname Dependents Covers age pname Dependents Purchaser Bad design Better design name Employees ssn lot Policies policyid cost Beneficiary policyid cost Policies name Employees ssn lot
Binary vs. Ternary Relationships (Contd.) ,[object Object],[object Object],[object Object],CREATE TABLE  Policies ( policyid  INTEGER , cost  REAL , ssn  CHAR(11)  NOT NULL , PRIMARY KEY  (policyid). FOREIGN KEY  (ssn)  REFERENCES  Employees, ON DELETE CASCADE ) CREATE TABLE  Dependents   ( pname  CHAR(20) , age  INTEGER , policyid  INTEGER , PRIMARY KEY  (pname, policyid). FOREIGN KEY  (policyid)  REFERENCES  Policies, ON DELETE CASCADE )
Views ,[object Object],CREATE  VIEW   YoungActiveStudents (name, grade) AS   SELECT  S.name, E.grade FROM   Students S, Enrolled E WHERE   S.sid = E.sid and S.age<21 ,[object Object],[object Object],[object Object]
Views and Security ,[object Object],[object Object]
View Definition ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example Queries ,[object Object],[object Object],create view  all_customer  as   ( select  branch_name, customer_name     from  depositor, account   where  depositor.account_number = account.account_number  ) union   ( select  branch_name, customer_name   from  borrower, loan   where  borrower.loan_number = loan.loan_number  ) select  customer_name from  all_customer where  branch_name =  'Perryridge'
Uses of Views ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Processing of Views ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
View Expansion ,[object Object],[object Object],[object Object],[object Object],[object Object]
With Clause ,[object Object],[object Object]
Complex Queries using With Clause ,[object Object],with   branch_total  ( branch _ name ,  value )  as   select   branch _ name ,  sum  ( balance )   from   account   group   by   branch _ name   with   branch _ total _ avg  ( value )  as   select   avg  ( value )   from   branch _ total   select  branch _ name   from   branch _ total ,  branch _ total_avg    where   branch_total.value >= branch_total_avg.value ,[object Object],[object Object]
Update of a View ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Formal Relational Query Languages ,[object Object],[object Object],[object Object]
Preliminaries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example Instances ,[object Object],[object Object],R1 S1 S2
Relational Algebra ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Projection ,[object Object],[object Object],[object Object],[object Object]
Selection ,[object Object],[object Object],[object Object],[object Object]
Union, Intersection, Set-Difference ,[object Object],[object Object],[object Object],[object Object]
Cross-Product ,[object Object],[object Object],[object Object],[object Object]
Joins ,[object Object],[object Object],[object Object],[object Object]
Joins ,[object Object],[object Object],[object Object]
Division ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Examples of Division A/B A B1 B2 B3 A/B1 A/B2 A/B3
Expressing A/B Using Basic Operators ,[object Object],[object Object],[object Object],[object Object],[object Object],A/B: all disqualified tuples
Find names of sailors who’ve reserved boat #103 ,[object Object],[object Object],[object Object]
Find names of sailors who’ve reserved a red boat ,[object Object],A query optimizer can find this, given the first solution! ,[object Object]
Find sailors who’ve reserved a red or a green boat ,[object Object],[object Object],[object Object]
Find sailors who’ve reserved a red  and  a green boat ,[object Object]
Relational Calculus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Domain Relational Calculus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DRC Formulas ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Free and Bound Variables ,[object Object],[object Object],[object Object],[object Object]
Find all sailors with a rating above 7 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Find sailors rated > 7 who have reserved boat #103 ,[object Object],[object Object]
Find sailors rated > 7 who’ve reserved a red boat ,[object Object],[object Object]
Find sailors who’ve reserved all boats ,[object Object]
Find sailors who’ve reserved all boats (again!) ,[object Object],[object Object],.....
Unsafe Queries,  Expressive Power ,[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Database design process
Database design processDatabase design process
Database design process
Tayyab Hameed
 
Database fundamentals(database)
Database fundamentals(database)Database fundamentals(database)
Database fundamentals(database)
welcometofacebook
 
Normalization in DBMS
Normalization in DBMSNormalization in DBMS
Normalization in DBMS
Prateek Parimal
 
1 - Introduction to PL/SQL
1 - Introduction to PL/SQL1 - Introduction to PL/SQL
1 - Introduction to PL/SQL
rehaniltifat
 
File systems versus a dbms
File systems versus a dbmsFile systems versus a dbms
File systems versus a dbms
RituBhargava7
 
Introduction to database & sql
Introduction to database & sqlIntroduction to database & sql
Introduction to database & sql
zahid6
 
2. Entity Relationship Model in DBMS
2. Entity Relationship Model in DBMS2. Entity Relationship Model in DBMS
2. Entity Relationship Model in DBMS
koolkampus
 
Introduction to DBMS(For College Seminars)
Introduction to DBMS(For College Seminars)Introduction to DBMS(For College Seminars)
Introduction to DBMS(For College Seminars)
Naman Joshi
 
Oracle basic queries
Oracle basic queriesOracle basic queries
Oracle basic queries
PRAKHAR JHA
 
DBMS Practical File
DBMS Practical FileDBMS Practical File
DBMS Practical File
Dushmanta Nath
 
Modern database management jeffrey a. hoffer, mary b. prescott,
Modern database management   jeffrey a. hoffer, mary b. prescott,  Modern database management   jeffrey a. hoffer, mary b. prescott,
Modern database management jeffrey a. hoffer, mary b. prescott,
BlackIce86
 
introdution to SQL and SQL functions
introdution to SQL and SQL functionsintrodution to SQL and SQL functions
introdution to SQL and SQL functions
farwa waqar
 
EER modeling
EER modelingEER modeling
EER modeling
Dabbal Singh Mahara
 
Oracle architecture with details-yogiji creations
Oracle architecture with details-yogiji creationsOracle architecture with details-yogiji creations
Oracle architecture with details-yogiji creations
Yogiji Creations
 
database
databasedatabase
Presentation on tablespaceses segments extends and blocks
Presentation on tablespaceses segments extends and blocksPresentation on tablespaceses segments extends and blocks
Presentation on tablespaceses segments extends and blocks
Vinay Ugave
 
Unit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 CompleteUnit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 Complete
Raj vardhan
 
Object relational database management system
Object relational database management systemObject relational database management system
Object relational database management system
Saibee Alam
 
Database Chapter 3
Database Chapter 3Database Chapter 3
Database Chapter 3
shahadat hossain
 
Integrity constraints in dbms
Integrity constraints in dbmsIntegrity constraints in dbms
Integrity constraints in dbms
Vignesh Saravanan
 

What's hot (20)

Database design process
Database design processDatabase design process
Database design process
 
Database fundamentals(database)
Database fundamentals(database)Database fundamentals(database)
Database fundamentals(database)
 
Normalization in DBMS
Normalization in DBMSNormalization in DBMS
Normalization in DBMS
 
1 - Introduction to PL/SQL
1 - Introduction to PL/SQL1 - Introduction to PL/SQL
1 - Introduction to PL/SQL
 
File systems versus a dbms
File systems versus a dbmsFile systems versus a dbms
File systems versus a dbms
 
Introduction to database & sql
Introduction to database & sqlIntroduction to database & sql
Introduction to database & sql
 
2. Entity Relationship Model in DBMS
2. Entity Relationship Model in DBMS2. Entity Relationship Model in DBMS
2. Entity Relationship Model in DBMS
 
Introduction to DBMS(For College Seminars)
Introduction to DBMS(For College Seminars)Introduction to DBMS(For College Seminars)
Introduction to DBMS(For College Seminars)
 
Oracle basic queries
Oracle basic queriesOracle basic queries
Oracle basic queries
 
DBMS Practical File
DBMS Practical FileDBMS Practical File
DBMS Practical File
 
Modern database management jeffrey a. hoffer, mary b. prescott,
Modern database management   jeffrey a. hoffer, mary b. prescott,  Modern database management   jeffrey a. hoffer, mary b. prescott,
Modern database management jeffrey a. hoffer, mary b. prescott,
 
introdution to SQL and SQL functions
introdution to SQL and SQL functionsintrodution to SQL and SQL functions
introdution to SQL and SQL functions
 
EER modeling
EER modelingEER modeling
EER modeling
 
Oracle architecture with details-yogiji creations
Oracle architecture with details-yogiji creationsOracle architecture with details-yogiji creations
Oracle architecture with details-yogiji creations
 
database
databasedatabase
database
 
Presentation on tablespaceses segments extends and blocks
Presentation on tablespaceses segments extends and blocksPresentation on tablespaceses segments extends and blocks
Presentation on tablespaceses segments extends and blocks
 
Unit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 CompleteUnit 1: Introduction to DBMS Unit 1 Complete
Unit 1: Introduction to DBMS Unit 1 Complete
 
Object relational database management system
Object relational database management systemObject relational database management system
Object relational database management system
 
Database Chapter 3
Database Chapter 3Database Chapter 3
Database Chapter 3
 
Integrity constraints in dbms
Integrity constraints in dbmsIntegrity constraints in dbms
Integrity constraints in dbms
 

Similar to Unit03 dbms

Unit03 dbms
Unit03 dbmsUnit03 dbms
Unit03 dbms
Praveen Kumar
 
Eer >r.model
Eer >r.modelEer >r.model
Eer >r.model
lavya3
 
Ch3_Rel_Model-95.ppt
Ch3_Rel_Model-95.pptCh3_Rel_Model-95.ppt
Ch3_Rel_Model-95.ppt
AtharvaBagul2
 
Keys.ppt
Keys.pptKeys.ppt
Keys.ppt
RohonPurkait
 
Unit 03 dbms
Unit 03 dbmsUnit 03 dbms
Unit 03 dbms
anuragmbst
 
DEE 431 Database keys and Normalisation Slide 2
DEE 431 Database keys and Normalisation Slide 2DEE 431 Database keys and Normalisation Slide 2
DEE 431 Database keys and Normalisation Slide 2
YOGESH SINGH
 
B.tech admission in india
B.tech admission in indiaB.tech admission in india
B.tech admission in india
Edhole.com
 
Dbms ii mca-ch4-relational model-2013
Dbms ii mca-ch4-relational model-2013Dbms ii mca-ch4-relational model-2013
Dbms ii mca-ch4-relational model-2013
Prosanta Ghosh
 
DBMS-Unit-2.pptx
DBMS-Unit-2.pptxDBMS-Unit-2.pptx
DBMS-Unit-2.pptx
Abhinayacheekati
 
2 rel-algebra
2 rel-algebra2 rel-algebra
2 rel-algebra
Mahesh Jeedimalla
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
Ashwini Rao
 
uniT 4 (1).pptx
uniT 4 (1).pptxuniT 4 (1).pptx
uniT 4 (1).pptx
YashWaghmare27
 
RDBMS
RDBMSRDBMS
RDBMS
NilaNila16
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
rahulnadola3
 
Introduction to SQL
Introduction to SQLIntroduction to SQL
Introduction to SQL
DHAAROUN
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
poovathi nps
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
pradnyamulay
 
ch3.ppt
ch3.pptch3.ppt
Ch 3.pdf
Ch 3.pdfCh 3.pdf
NMEC RD_UNIT 1.ppt
NMEC RD_UNIT 1.pptNMEC RD_UNIT 1.ppt
NMEC RD_UNIT 1.ppt
IswaryaPurushothaman1
 

Similar to Unit03 dbms (20)

Unit03 dbms
Unit03 dbmsUnit03 dbms
Unit03 dbms
 
Eer >r.model
Eer >r.modelEer >r.model
Eer >r.model
 
Ch3_Rel_Model-95.ppt
Ch3_Rel_Model-95.pptCh3_Rel_Model-95.ppt
Ch3_Rel_Model-95.ppt
 
Keys.ppt
Keys.pptKeys.ppt
Keys.ppt
 
Unit 03 dbms
Unit 03 dbmsUnit 03 dbms
Unit 03 dbms
 
DEE 431 Database keys and Normalisation Slide 2
DEE 431 Database keys and Normalisation Slide 2DEE 431 Database keys and Normalisation Slide 2
DEE 431 Database keys and Normalisation Slide 2
 
B.tech admission in india
B.tech admission in indiaB.tech admission in india
B.tech admission in india
 
Dbms ii mca-ch4-relational model-2013
Dbms ii mca-ch4-relational model-2013Dbms ii mca-ch4-relational model-2013
Dbms ii mca-ch4-relational model-2013
 
DBMS-Unit-2.pptx
DBMS-Unit-2.pptxDBMS-Unit-2.pptx
DBMS-Unit-2.pptx
 
2 rel-algebra
2 rel-algebra2 rel-algebra
2 rel-algebra
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
 
uniT 4 (1).pptx
uniT 4 (1).pptxuniT 4 (1).pptx
uniT 4 (1).pptx
 
RDBMS
RDBMSRDBMS
RDBMS
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
 
Introduction to SQL
Introduction to SQLIntroduction to SQL
Introduction to SQL
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
 
ch3.ppt
ch3.pptch3.ppt
ch3.ppt
 
Ch 3.pdf
Ch 3.pdfCh 3.pdf
Ch 3.pdf
 
NMEC RD_UNIT 1.ppt
NMEC RD_UNIT 1.pptNMEC RD_UNIT 1.ppt
NMEC RD_UNIT 1.ppt
 

More from arnold 7490

Les14
Les14Les14
Les13
Les13Les13
Les11
Les11Les11
Les10
Les10Les10
Les09
Les09Les09
Les03
Les03Les03
Les02
Les02Les02
Les01
Les01Les01
Les12
Les12Les12
Unit 8 Java
Unit 8 JavaUnit 8 Java
Unit 8 Java
arnold 7490
 
Unit 6 Java
Unit 6 JavaUnit 6 Java
Unit 6 Java
arnold 7490
 
Unit 5 Java
Unit 5 JavaUnit 5 Java
Unit 5 Java
arnold 7490
 
Unit 4 Java
Unit 4 JavaUnit 4 Java
Unit 4 Java
arnold 7490
 
Unit 3 Java
Unit 3 JavaUnit 3 Java
Unit 3 Java
arnold 7490
 
Unit 2 Java
Unit 2 JavaUnit 2 Java
Unit 2 Java
arnold 7490
 
Unit 1 Java
Unit 1 JavaUnit 1 Java
Unit 1 Java
arnold 7490
 

More from arnold 7490 (20)

Les14
Les14Les14
Les14
 
Les13
Les13Les13
Les13
 
Les11
Les11Les11
Les11
 
Les10
Les10Les10
Les10
 
Les09
Les09Les09
Les09
 
Les07
Les07Les07
Les07
 
Les06
Les06Les06
Les06
 
Les05
Les05Les05
Les05
 
Les04
Les04Les04
Les04
 
Les03
Les03Les03
Les03
 
Les02
Les02Les02
Les02
 
Les01
Les01Les01
Les01
 
Les12
Les12Les12
Les12
 
Unit 8 Java
Unit 8 JavaUnit 8 Java
Unit 8 Java
 
Unit 6 Java
Unit 6 JavaUnit 6 Java
Unit 6 Java
 
Unit 5 Java
Unit 5 JavaUnit 5 Java
Unit 5 Java
 
Unit 4 Java
Unit 4 JavaUnit 4 Java
Unit 4 Java
 
Unit 3 Java
Unit 3 JavaUnit 3 Java
Unit 3 Java
 
Unit 2 Java
Unit 2 JavaUnit 2 Java
Unit 2 Java
 
Unit 1 Java
Unit 1 JavaUnit 1 Java
Unit 1 Java
 

Recently uploaded

Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Aggregage
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
James Anderson
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Zilliz
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial ApplicationsLarge Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
Rohit Gautam
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
SOFTTECHHUB
 
Data structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdfData structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdf
TIPNGVN2
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems S.M.S.A.
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Matthew Sinclair
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
danishmna97
 
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Zilliz
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Matthew Sinclair
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Albert Hoitingh
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Quotidiano Piemontese
 

Recently uploaded (20)

Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
 
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalizationFull-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial ApplicationsLarge Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
 
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
 
Data structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdfData structures and Algorithms in Python.pdf
Data structures and Algorithms in Python.pdf
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdfUni Systems Copilot event_05062024_C.Vlachos.pdf
Uni Systems Copilot event_05062024_C.Vlachos.pdf
 
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 202420240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
 
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
 
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practicesNational Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
 

Unit03 dbms

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52. Examples of Division A/B A B1 B2 B3 A/B1 A/B2 A/B3
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67.

Editor's Notes

  1. 3
  2. 4
  3. 9
  4. 15
  5. 16
  6. 10
  7. 5
  8. 6
  9. 6
  10. 7
  11. 7
  12. 13
  13. 14
  14. 3 The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This covers Lectures 1 and 2 (of 6) in Module (5). Module (1): Introduction (DBMS, Relational Model) Module (2): Storage and File Organizations (Disks, Buffering, Indexes) Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security) Module (4): Relational Implementation (Query Evaluation, Optimization) Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning) Module (6): Transaction Processing (Concurrency Control, Recovery) Module (7): Advanced Topics
  15. 5
  16. 6
  17. 7
  18. 8
  19. 9
  20. 10
  21. 11
  22. 12
  23. 13
  24. 7
  25. 8
  26. 18
  27. 22
  28. 4
  29. 5
  30. 6
  31. 7
  32. 8
  33. 9
  34. 10
  35. 11
  36. 12
  37. 13
  38. 14
  39. 15
  40. 16
  41. 17
  42. 18
  43. 19