Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview

Description

Relational databases are the backbones of modern businesses in processing transactions and storing customer data. Most organisations usually deploy several relational databases for operational convenience. It is quite often necessary to integrate the information existing in different relational databases for planning and decision making. Data warehouses are built to facilitate planning and decision making in businesses integrating data from different relational databases. Online analytical processing (OLAP) is a technology that uses a data warehouse for answering aggregation queries often used in planning. While relational databases hold important transactional information of a business, the success of a business quite often depends on advanced planning and development of strategies based on customer behaviour. Data mining technologies are used for discovering such patterns and trends in data stored in relational databases. This unit introduces the key mechanisms in data warehousing and OLAP. It discusses logical and physical design of data warehouses including star schema, snowflake schema, data marts, partitioning and materialised views. Students study the use of data warehouses through a study of the OLAP technology including the multidimensional OLAP (MOLAP) and relational OLAP (ROLAP) architectures, OLAP operations and structured query language (SQL) support for OLAP.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Details for undergraduate courses
  • Level 3 core unit in the Data Science; Computing and Data Science major sequences
  • Level 3 elective
Outcomes

Students are able to (1) understand that discovering and extracting knowledge from a massive amount of data is a key problem in many scientific and business disciplines; (2) demonstrate a thorough understanding of key data warehousing and online analytical processing technologies; and (3) apply key data warehousing concepts in designing solutions for business data analytics.

Assessment

Indicative assessments in this unit are as follows: (1) two project assignment and (2) final examination. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Siwen Luo
Unit rules
Prerequisites
CITS1402 Relational Database Management Systems
or CITX1402 Relational Database Management Systems
and CITS1401 Computational Thinking with Python
Incompatibility
CITS5504 Data Warehousing
Contact hours
lectures: 2 hours per week
labs: 2 hours per week
Text

Han, J. and Kamber, M. Data Mining: Concepts and Techniques, 2nd edn: Elsevier/Morgan Kaufmann 2006

  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
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  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.