CITS5504 Data Warehousing

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 1UWA (Perth)Face to face
Content
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.
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
Typically this unit is assessed in the following ways: (1) practical projects and (2) 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 Jianxin Li
Unit rules
Prerequisites:
enrolment in the Master of Information Technology (62510)
or
the Master of Data Science (62530)
Incompatibility:
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
Texts

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

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