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unitinfo
This page provides helpful information about many coursework units offered by
Computer Science and Software Engineering
in 2023.
The information here is not official -
for official information please see the
current UWA Handbook.
Instead, it will help students to prepare for their future units,
before the beginning of each semester,
and before they have access to
UWA's
Learning Management System (LMS).
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About the unit CITS3401 Data Warehousing (1st semester 2023)
Unit 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.
Unit 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.
Unit coordinator:
Unit homepage:
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Unit is offered in these majors and courses:
Indicative weekly topics:
| week 1 |
Introduction to Data Warehouse |
| week 2 |
Data Modelling for Data Warehousing and OLAP |
| week 3 |
Data Cube Technologies |
| week 4 |
Extract, Transform and Load (ETL) |
| week 5 |
Practical Concerns of Dimensional Modelling |
| week 6 |
Data Marts and Metadata |
| week 7 |
Data Quality, Profiling, Types and Reduction |
| week 8 |
Frequent Patterns and Association Rule Mining |
| week 9 |
Pattern-based Classification and Supervised Learning |
| week 10 |
Density and Partition based Data Clustering |
| week 11 |
Data Reduction and Enhancement |
| week 12 |
Efficient Cube Computation and Unit Review |
Indicative assessment:
2 Projects + Final Exam
Software required for this unit:
Students are able to undertake their laboratory exercises and projects in laboratories in the CSSE building, but most students also complete work on their own laptops.
The following software is required to successfully complete this unit:
Visual Studio, SQL Server, Microsoft SQL Server Management Studio, PowerBI, WEKA
Operating system(s) used in this unit:
Different units will use different operating systems for their teaching - for in-class examples, laboratory exercises, and programming projects.
If an operating system is REQUIRED, it will be used when marking assessments.
Windows REQUIRED (win10 or later);Windows suitable (win10 or later)
This information last updated 8:51am Fri 21st Apr 2023
