MONASH
INFORMATION TECHNOLOGY
Week 1 Session 1 Introduction FIT2094 FIT3171 Databases
Summer Semester B 2021
Unit Overview
Unit purpose/background
An introduction to databases, mostly RELATIONAL databases (RDBMS)
Require programming background but NO expected background in
databases
Student time commitment
Monash University 6 credit point unit = 12 hours of work per session
Schedule: two sessions per week = 24 hours of work per week
4 hrs workshop
4 hrs tutorial
16 hrs of your own assigned time (pre workshop reading, pre
workshop quiz, Moodle workshop lessons, completing tutorial activities, assignments. etc.)
2
Your FIT2094 FIT3171 2021 Summer B Workshop Leader
3
Email Contact
Email Contact
During the semester your first contact must be your tutor, unless the matter is a unit administration matter
tutors will be assigned during session 2 in week 1 and will supply you with their email address, also available from Moodle Unit Information
Admin matters (absences, class issue etc) please email:
[email protected]
Note these units email requirements:
When you contact staff via email, please ensure you clearly include your full name, unit code and tutorial number as part of every email you send. This will ensure we can respond as quickly and accurately as possible.
You must email from your Monash University email account
email which does not comply will not be responded to
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Study Program
Week
0
1
Session
0
Activities
Assessments
No formal assessment or activities are
1
undertaken in week 0.
Introduction to Database
Pre Workshop Quizzes (5%) Weeks 1 to 6
2
2
Workshop Activities (5%) Weeks 1 to 6
1
Database Design I Conceptual Modelling
3
Relational Model
2
4
Normalisation
Assignment 1A (5%) Wed 4PM Melbourne
3
Database Design II Logical level modelling
4
5
6
Time (Week 3)
3
Creating and Populating the Database
7
SQL Part I SQL Basic
4
8
Update, Delete and Transaction Management
Assignment 1B (15%) Wed 4PM
Melbourne Time (Week 4)
5
5
6
9
10
11
SQL Part II SQL Intermediate and PL SQL
SQL Part III SQL Advanced
Database Connectivity, Web Technologies
6
12
Data Warehousing, Big Data and Future
Directions
Assignment 2 (20%) Fri 5PM Melbourne Time
5 sessions Database Design
5 sessions Using databases
5
Traditional Teaching Method
6
Flipped Classroom Full Picture
7
Flipped Classroom Scenario
Process starts with assigned readings followed by a quiz to test understanding and provide feedback
Workshop then poses questions as part of the lesson flow Workshop is an online Moodle lesson
Student responds via the Moodle based lesson questions
Workshop Q and A sessions
online via Zoom
provide an opportunity to gain an overview of the content and seek clarification
be part of a wider discussion with your peers on the material
may include poll/questions to gauge understanding
Apply knowledge in tutorial and complete tutorial tasks feedback provided on attempt via sample solutions
8
Things are different
Pre-workshop activities (readings/quizzes) are crucial.
Your workshop experience will depend on your preparation.
Completing the workshop activities and attending the Workshop Q&A sessions are a key part of your learning
Our workshop slides are NOT your notes!
Create your own notes during pre-workshop reading.
Annotate difficult concepts, revisit the annotation after you have completed the workshop and workshop Q&A.
You should be prepared before the workshop and workshop Q&A, then think and do during the workshop and ask questions during the workshop Q&A.
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Why Flipped Learning?
Engage students to take ownership of their learning
Build and test ones understanding in a supportive environment.
Develop critical thinking, communication and reflection skills.
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Overview
An overview of relational database management systems (RDBMS)
11
Lets travel back to 1960s
Relational databases do not exist yet
Lets create a database to record the information on Monash students
What kind of approaches do we have? What kinds of problems are involved?
12
What is a database?
13
How do we structure our data?
14
Data Redundancy a student data spreadsheet
What are the issues/problems related to storing this data in a spreadsheet form as above?
15
Data Redundancy a student data spreadsheet
16
Why do we have so many problems in our spreadsheet data?
The structure of the data causes some data management problems or data anomalies
insert anomaly cannot add a new unit until a student is enrolled
update anomaly updating may require update of multiple rows with risk
of missing some items (eg Cindy Sheen to Cinderella Sheen)
Delete anomaly when the last enrolment in a unit is deleted other
related data (the unit details) will be lost .
The software was not designed to deal with the type of reporting required.
17
How do we solve it?
18
DATABASE
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1970: Relational model
An IBM scientist
Proposed and developed the relational model
Also proposed normalisation forms
Resistance from IBM to implement his model
Turing award (1981)
Relational model in session 3
Normalisation in session 4
E. F. Codd, A Relational Model of Data for Large Shared Data Banks, Comm. Of ACM, 1970
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1974: SQL
Developed at IBM
Initially called SEQUEL (Structured
English QUEry Language)
Doesnt strictly follow Codds theory
Oracle: the first commercially available
implementation of SQL in 1979
SQL in sessions 7, 8, 9 & 10
D Chamberlin, R Boyce, SEQUEL: A structured English query language, ACM SIGFIDET, 1974
21
1976: Conceptual model
Proposed Entity-Relationship Model (ER diagram)
A systematic process to design a relational database
Database design process in session 2 &5
Peter Chen, The entity-relationship modeltoward a unified view of data, ACM TODS, 1976
22
1979: Oracle
Inspired by Codds ideas
First commercial release in 1979
Most popular RDBMS
Introduced PL/SQL in 1988 (Procedural Language/SQL)
Oracle SQL in session 7, 8, 9 & 10
23
1981: Transactions management
Introduced transaction management
Turing award (1998)
Presumed lost at sea in 2007
Transaction management in session 8
Jim Gray, The Transaction Concept: Virtues and Limitations , VLDB, 1981
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Data Management Today
Relational databases are still very popular. But
Social Networks (Facebook, Twitter, Foursquare etc.) Multimedia data (YouTube, Pinterest, Facebook etc.) Data streams (Twitter, computer networks)
Spatial data (Road networks, Google Earth, Space etc.) Textual data
Web data Big Data
https://goo.gl/zMxG3b
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In Perspective
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27
28
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RANK
DBMS
TYPE
INTRODUCED
1
Commercial, Relational DBMS
1979
2
Open source, Relational DBMS
1995
3
Commercial, Relational DBMS
1989
4
Open source, Relational DBMS
1996
5
Open Source, Nosql Document Store
2009
6
DB2
Commercial, Relational DBMS
1983
March 2020
30
Relational database systems in action: End-users view
31
Database Systems in Action Developers View
32
Developing Application with Database
33
Our Database Systems Environment
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