DATA WAREHOUSE CONCEPTS AND DESIGN

CSE5DWD

2019

Credit points: 15

Subject outline

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 admitted into Master of Business Information Management and Systems

Co-requisitesN/A

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. Comprehensively design a suitable data warehouse solution using various dimensional modelling techniques for a given problem

Activities:
Lab class and lecture discussions, assignment practice and online learning

02. Critically appraise and compare different data warehouse modelling approaches for real world industry projects

Activities:
Lab class and lecture discussions, assignment practice and online learning

03. Extract, transform and load source data for a data warehouse.

Activities:
Lab class and Lecture discussions; assignment practice.

04. Critique Online Analytical Processing performance on different data warehouse architectures

Activities:
Lab class and lecture discussions, online learning

05. Evaluate data warehouse design to improve the user's satisfaction level

Activities:
Lab class and lecture discussions, assignment practice and online learning

Subject options

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Start date between: and    Key dates

Melbourne, 2019, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJinli Cao

Class requirements

Unscheduled Online Class Week: 10 - 22
One 3.0 hours unscheduled 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 face-to-face.

Assessments

Assessment elementComments% ILO*
One assignment - data warehouse design using dimensional modelling techniques (equiv to 1800 words)30 01, 02, 03, 05
One 2-hour examinationHurdle requirement: To pass the subject, a pass in the examination is mandatory. This is to meet basic knowledge requirement for the subject.50 01, 02, 03, 04
Ten weekly online quizzes (each quiz lasts for 10 minutes, 1700 words equivalent total)20 01, 02, 03, 04, 05