Introduction to Database Technology course

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Transcript Introduction to Database Technology course

Introduction to Database
Technology course
DT228/3
Semester 1
Breakdown of course
• This course is valued at 5 ECTS points.
• There are 36 contact hours, leaving 64 self-study hours.
• Weekly contact hours:
– 1 hour lecture
– 1 hour tutorial
– 1 hour lab
• Self-Study expectations:
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Finish all lab / tutorial exercises assigned to you.
Undertake one lab test
Undertake one assignment, as part of a group.
Learn the theoretical and practical content of the course.
Lectures
• Design
– Designing a data model using Normalisation
• Practical
– Standard SQL and Oracle PL/SQL to allow for
lab classes.
• Theoretical
– Architecture and usage of RDBMS.
– Theory of concurrent usage, architecture,
data protection, security, recovery.
Tutorials
• Practice of database schema design.
• Problems relating to concurrency and
transaction design.
• Design of a relational model (in a group)
for a system from a set description.
• Design and specification of queries and
transactions that would be required by the
users of the designed system.
Practicals
• Introduction to Oracle SQL*Plus.
• Implementation of standard query types on a
populated schema.
• Creation and population of a schema as
designed in tutorials.
• Implementation of task-based queries.
• Implementation of task-based transactions,
using commit and roll-back, taking concurrency
issues into account.
• Some time will be given to doing your lab test
and assignment.
Assessment
• 30% practical:
– 10% lab test.
– 20% group system implementation.
• 70% examination (2 hours):
– 4 questions, answer 3.
• Q1 compulsory, worth 40%,
– based on tutorial and practical work.
• Q2, 3 and 4 worth 30% each.
– Based on tutorial, practical and theory work.
You will learn
• Data modelling
• Relational database theory
• Use of Oracle Database 10g, using the
SQL*Plus and iSQL*Plus tools.
• Design and implement:
– Queries in SQL
– Transactions in SQL
– Procedures, Functions, Packages and
Triggers in PL/SQL
Data modelling
• How to model data from the meaning of
the stored data.
• Derive a data model from system data
entries and outputs.
• Use Data Normalisation to improve your
model.
Models you will see
• Sample schema
– Builders’ providers – I will use this to
demonstrate techniques
– Football competition coordination – We will
build this through simple exercises
• YOUR GROUP schema
– YOU and two others from your class will
design and build this.