CITS3401 Data Warehousing

6 points
(see Timetable)
Semester 1UWA (Perth)Face to face
Details for undergraduate courses
  • Level 3 core unit in the Data Science major sequence
  • The area of knowledge for this unit is Mathematical and Physical Sciences
  • Category B broadening unit for students
  • Level 3 elective
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 not available in this unit except in the case of a bachelor's pass degree student who has obtained a mark of 45 to 49 overall 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 Zeyi Wen
Unit rules
CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
CITS2232 Databases; for pre-2012 courses: CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases)
CITS2232 Databases
CITS4243 Advanced Databases
Contact hours
lectures: 2 hours per week; practical classes: 2 hours per week; labs: 2 hours per week
Unit Outline
Semester 1-2020 [SEM-1-2020]

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

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