Module: Data Warehousing and Multidimensional Analysis

Module Specifications

Academic Year 2026 - 2027

Module Title	Data Warehousing and Multidimensional Analysis
Module Code	CSC1204 (ITS: CA270)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

A Data Warehouse is the model or structure that supports data mining and decision support through Online Analytical Processing (OLAP). This module teaches students how to construct Data Warehouses by understanding their structures and the concept of multi-dimensional modelling.

Learning Outcomes

1. Build Data Warehouses for different application areas.
2. Deploy the Data Warehouse Bus Matrix to create individual data marts.
3. Design a multi-dimensional schema.
4. Explain the structures and functions that deliver OLAP for multi-dimensional schemas.
5. Apply classification algorithms to mining data.
6. Apply clustering algorithms to interpret data.
7. Appy ARM algorithms in the area of data mining.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	No Description
Assignment Completion	50	No Description
Independent Study	51	No Description
Total Workload: 125

Section Breakdown
CRN	12223	Part of Term	Semester 1
Coursework	20%	Examination Weight	80%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mark Roantree	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Data Warehousing assignment, involving OLAP and star schema design.	20%	n/a
Formal Examination	2-hour end of year exam	80%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	1,
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Overview
Introduction to Data Warehouses

Data Warehouse Architecture
ETL and System Components

Multidimensional Data Model
Data Cubes, Different schema types

Dimensional Design
4-step process to designing the data warehouse

Data Warehouse Deployment
Using the Data Warehouse Bus Matrix

Data Warehouse Implementation
Indexing OLAP and Data Cube Computation

Efficient Computation of Data Cubes
Algorithms and methods for efficient cube computation

Web Data Warehouses
Incoporating web generated data into the warehouse

Warehouse Case Studies
An examination of a number of Warehouse Implementations

Indicative Reading List

Books:

Han & Kamber: 0, Data Mining: Concepts and Techniques, 3 & 4, Morgan Kaufmann,
Kimball & Ross: 0, The Data Warehouse Toolkit, Wiley,

Articles:
None

Other Resources

None