Course Title: Knowledge and Data Warehousing

Part A: Course Overview

Course Title: Knowledge and Data Warehousing

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

ISYS1072

City Campus

Postgraduate

140H Computer Science & Information Technology

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014

ISYS1073

City Campus

Undergraduate

140H Computer Science & Information Technology

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014

Course Coordinator: Dr. Jenny Zhang

Course Coordinator Phone: +61 3 9925 2774

Course Coordinator Email: xiuzhen.zhang@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Prerequisite: Database Concepts AND Advanced Programming

You may not enrol in this course unless it is explicitly listed in your enrolment program summary.

Note it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For your information the RMIT Course Requisites policy can be found at Course requisites – 7.29.1.6: http://www.rmit.edu.au/browse;ID=twx09y07zi1c
 


Course Description

The course allows you to explore the principles and practice of up-to-date conceptual and practical knowledge on recent developments in database technology, specifically data warehousing. Topics include overview of data warehousing, Data warehouse design, OLAP technologies, Data warehousing in practice, and Data mining in data warehouses.


Objectives/Learning Outcomes/Capability Development

Development of student graduate capabilities is an on-going process that takes place in all courses and over the period of the whole program. In this course you will specifically address the following capabilities in:
• enabling knowledge (deductive databases, data warehousing and data mining),
• critical analysis (in implementing deductive database solutions, undertaking data mining exercises, and in setting up and managing data warehouses), and
• responsibility (appropriate use of stored data, especially business data).
 


   On successful completion of this course, you will be able to:

• analyse a database problem and determine whether a deductive system will be superior to a purely relational system by comparing the relational approach to a deductive solution;
• communicate the evolution and reasons for how and why a deductive approach differs from the pure relational model;
• implement a deductive solution to a database problem using a commercial database system that supports these facilities;
• demonstrate your understanding of optimisations used in the construction of actual deductive systems by solving problems using a variety of optimisation strategies and commenting on the strengths and limitations of optimisation strategies;
• define what knowledge discovery and data mining are;
• define the concept, structure and major issues of data warehousing;
• develop general awareness of data warehousing project management;
• apply multi-dimensional modelling techniques in designing data warehouses;
• apply the online analytical processing (OLAP) technology for decision support;
• apply data cubing techniques; and
• use knowledge discovery in data warehouses.


Overview of Learning Activities

Lectures will focus on what data warehousing systems are and what they should and ought to be capable of achieving in the context of a usable commercial system. The tutorial/laboratory classes, however, will focus on the reality of what current database systems can and do actively deliver. As such, real commercial database systems will be studied and employed for practical exercises and assignments which aim to demonstrate how you can use the commercial system to solve real problems.


Overview of Learning Resources

You will be able to access course information and learning materials through myRMIT and will be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

Learning resources include comprehensive overheads that cover the material delivered in the lecture series. There are weekly handouts for the tutorial/lab exercises that introduce the commercial database systems through smaller hands-on exercises in a laboratory environment. The database and data warehousing systems themselves also form an important part of the learning resources, as do a set of recommended textbooks, which students should use to obtain a different description of a topic discussed in the lectures. References to the appropriate texts are provided in the course of lecture delivery. Some texts are on reserve in the library to ensure minimal access.
 


Overview of Assessment

There are two tests (one for the deductive component and the other for the data warehousing component). In the tests, you will be required to demonstrate that you recognise when to choose a given solution and why. You will be required to analyse an optimisation algorithm via the presentation of an example problem that needs to be solved in the context of that algorithm. You will show how to encode a solution or model a system using pure deductive database.
There are two major assignments (one for each component as above) where you will demonstrate that you can use a commercial system by designing and implementing a real-world mini problem.
Both components (tests and assignments) are critical in evaluating that you have achieved the outcomes that are expected, and hence they are deemed as hurdles.

For standard assessment details, including deadlines, weightings, and hurdle requirements relating to Computer Science and IT courses see: http://www.rmit.edu.au/compsci/cgi