CITS5504 Data Warehousing

6 points
(see Timetable)
Semester 1UWA (Perth)Face to face
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.
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.
Indicative assessments in this unit are as follows: (1) project assignment; (2) mid-semester test; and (3) final examination. Further information is available in the unit outline.

Supplementary assessment is only available in this unit in the case of a student who has obtained a mark of 45 to 49 and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
Unit Coordinator(s)
Dr Wei Liu
Unit rules
Enrolment in (62530 Master of Data Science
62510 Master of Information Technology
42630 Master of Business Analytics)
(CITS1402 Relational Database Management Systems
BUSN5101 Programming for Business
BUSN5002 Fundamentals of Business Analytics).
CITS4243 Advanced Databases, CITS3401 Data Warehousing and Data Mining (formerly CITS3401 Data Exploration and Mining)
Contact hours
lectures: 2 hours per week; labs: 2 hours per week
Unit Outline
Semester 1-2020 [SEM-1-2020]

To be advised by the School of Computer Science and Software Engineering.

  • 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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  • Books and other material wherever listed may be subject to change. Book lists relating to 'Preliminary reading', 'Recommended reading' and 'Textbooks' are, in most cases, available via the Booktopia Textbook Finder, which has the functionality to search by course code, course, ISBN and title, and may also be posted or available at the appropriate school's administrative offices. Where texts are listed in the unit description above, an asterisk (*) indicates that the book is available in paperback.