DATA WAREHOUSE CONCEPTS AND DESIGN

CSE5DWD

2018

Credit points: 15

Subject outline

Data warehouse (DW) underpins most enterprise systems like enterprise resource planning, customer relationship management and supply chain management systems.A DW is designed to provide information from a variety of sources within and outside an organisation to the enterprise systems. This subject introduces students to evolution of data warehouse technology, data warehouse terms and   concepts, data warehouse design, data sourcing, organisational issues involved with designing and implementing a data warehouse. Especially, the multidimensional modelling with various data warehouse design schemas such as star schema and snowflake schema are the foci of the subject. Furthermore,related important technologies of Extract-Transformation-Load (ETL) system including 34 subsystems are discussed and studied. The different Online Analytical Processing (OLAP) architectures and approaches are analysed,compared and evaluated in the subject. The research issues on the performance of data warehouse and OLAP techniques are discussed and investigated.  Several real world case studies are used to explain and illustrate various aspects of this subject.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorJinli Cao

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites BUS5BID or CSE2DBF or CSE4DBF or students enrolled in the Master of Business Analytics or Graduate Diploma of Business Analytics

Co-requisites For students enrolled in Master of Business Analytics or Graduate Diploma of Business Analytics - BUS5WB Data Warehousing and Big Data

Incompatible subjects CSE4DWD

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsThe Data Warehouse ToolkitPrescribedKimball, R and Ross, M, 2013WILEY
ReadingsThe Data Warehouse Lifecycle Toolkit, 2nd edRecommendedKimball, R, et alWILEY, 2008

Graduate capabilities & intended learning outcomes

01. Explain the role of the data warehouse within an enterprise system

Activities:
Lecture, tutorial discussions
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

02. Analyse a given problem and design a suitable data warehouse solution using various dimensional modelling techniques

Activities:
Tutorials, assignment, exam
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Literacies and Communication Skills (Writing)

03. Appraise and compare different data warehouse modelling approaches for real world industry projects

Activities:
Tutorials, assignment, exam
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Literacies and Communication Skills (Writing)

04. Explain and recommend solutions for the issues involved with sourcing data for the data warehouse tool

Activities:
Tutorials, Lectures, exam
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

05. Analyse critically OLAP performance on different data warehouse architectures

Activities:
Tutorials, exam
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Literacies and Communication Skills (Writing)

06. Research and apply established theories to improve data warehouse design

Activities:
Tutorials, assignment, exam
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Inquiry and Analytical Skills (Creative Problem-solving,Inquiry/Research)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

07. Explain the principle involved in managing a data warehouse project

Activities:
Tutorials, assignment, exam
Related graduate capabilities and elements:
Literacies and Communication Skills (Writing)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Personal and Professional Skills (Ethical behaviour,Adaptability Skills,Study and Learning Skills)
Discipline -Specific Knowledge and Skills (Discipline-Specific Knowledge and Skills)

Subject options

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Start date between: and    Key dates

City Campus, 2018, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJinli Cao

Class requirements

Scheduled Online Class Week: 10 - 22
One 3.0 hours scheduled online class per week on weekdays during the day from week 10 to week 22 and delivered via online.

Laboratory Class Week: 10 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
One 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via online.

Assessments

Assessment elementComments% ILO*
One assignment - data warehouse design using dimensional modelling techniques (1800 words)In order to pass the subject, students must pass this assignment. The hurdle 50% is applied to this assessment item.30 01, 02, 03, 04, 06, 07
One 2-hour examinationIn order to pass the subject, students must pass 45% of this exam. The hurdle is applied to this assessment item.50 01, 02, 03, 04, 05, 06, 07
Weekly Quiz & review questions (1000 words)20 01, 02, 03, 04, 05, 06, 07

Melbourne, 2018, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJinli Cao

Class requirements

Scheduled Online Class Week: 10 - 22
One 3.0 hours scheduled online class per week on weekdays during the day from week 10 to week 22 and delivered via online.

Laboratory Class Week: 10 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
One 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via online.

Assessments

Assessment elementComments% ILO*
One assignment - data warehouse design using dimensional modelling techniques (1800 words)In order to pass the subject, students must pass this assignment. The hurdle 50% is applied to this assessment item.30 01, 02, 03, 04, 06, 07
One 2-hour examinationIn order to pass the subject, students must pass 45% of this exam. The hurdle is applied to this assessment item.50 01, 02, 03, 04, 05, 06, 07
Weekly Quiz & review questions (1000 words)20 01, 02, 03, 04, 05, 06, 07