Prerequisites for Module
None except for course entry requirements.
Corequisite Modules
None.
Precluded Modules
None.
Aims of Module
To provide key concepts and principles of data warehousing techniques and to develop specialised knowledge in areas that demonstrate the interaction and synergy between research and practices of distributed large-scale data stores.
Learning Outcomes for Module
On completion of this module, students are expected to be able to:
| 1. |
Critically appraise data warehousing design principles, practices and standards.
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| 2. |
Compare and contrast methodologies that are at the forefront in data warehousing design and development.
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| 3. |
Design a multidimensional analysis of business data and interpret results from complex trend analysis using state-of-the-art modeling algorithms.
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| 4. |
Create a data warehousing solution by adopting and extending methods that are informed by current research and industry practices.
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Indicative Module Content
Data Capture, data cleaning, data conformation, data integration, data federation and data virtualisation.
Concepts and benefits associated with data warehousing.
Conventional, spatial and temporal data warehouses.
Architecture of a data warehouse.
Data warehouse design.
Tools for Data warehousing.
State of the art in data warehousing, including data warehousing in the cloud.
| Data warehousing with big data Case studies.
Indicative Student Workload
Contact Hours
| Part Time | Laboratories
| 24 | Lectures/ Tutorials
| 24 |
Directed Study
| | Assessment
| 3 | Coursework Preparation
| 20 | Directed Reading
| 30 |
Private Study
| | Private Study
| 49 |
Mode of Delivery
Key concepts are introduced and illustrated through lectures and directed reading. The understanding of students is tested and further enhanced through interactive tutorials. In the laboratories the students will progress through a sequence of exercises to further their understanding and gain practical experience of data warehousing.
Assessment Plan
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Learning Outcomes Assessed
| | Component 1 | 1,2
| | Component 2 | 3,4
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Component 2 - This is a coursework involving the development of a data warehousing application worth 50% of the total module assessment.
Component 1 - This is a written report worth 50% of the total module assessment.
Indicative Bibliography
| 1. | CONNOLLY, T. and BEGG, C., 2015. Database Systems: A Practical Approach to Design, Implementation and Management. Pearsons.
| | 2. | KIMBALL, R., ROSS, M., 2013. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition). John Wiley & Sons, Inc.
| | 3. | MOHANTI, S., 2013. Big Data imperatives- enterprise big data warehouse, BI implementations and analytics. Apress.
| | 4. | SILVERS, F., 2008. Building and maintaining a data warehouse. CRC Press.
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