Module: Data Warehousing & OLAP

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Warehousing & OLAP
Module Code	CA270 (ITS) / CSC1032 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Mark Roantree
Module Teachers	-
NFQ level	8	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

None

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. Be able to build Data Warehouses for different application areas.
2. Be able to deploy the Data Warehouse Bus Matrix to create individual data marts.
3. Be able to design a multi-dimensional schema.
4. To understand the structures and functions that deliver OLAP for multi-dimensional schemas.
5. Be able to apply classification algorithms to mining data.
6. Be able to apply clustering algorithms to interpret data.
7. Be able to apply 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

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

Assessment Breakdown
Continuous Assessment	20%	Examination Weight	80%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Data Warehousing assignment, involving OLAP and star schema design.	20%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

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

Other Resources

None