The VLE (NETED)
The Internet
Word Processing software for use in the coursework
Spreadsheet software like EXCEL/SPSS
Printed and electronic journals.

Simson, G. C. (2004) Data Modeling Essentials (The Morgan Kaufmann Series in Data Management Systems)
Witten, I. A. Frank, E, Hall, M. A. (2011) Data Mining: Practical Machine Learning Tools and Techniques
Loshin, D. (2008) Master Data Management, (The MK/OMG Press)
Corr, L. Stagnitto, J. (2011) Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Robshaw, M., Billet,O. (2008), New Stream Cipher Designs, Springer
Clark, R. M. (2009) Intelligence Analysis: A Target-Centric Approach; CQ Press;
Cusick, T. W., Ding, C. and Renvall, A. R. (2004) Stream Ciphers and Number Theory, Elsevier Science

Journals
Journals in Database Management & Info Retrieval - Springer
IGI Global: Journal of Database Management
International Journal of Database Management Systems
Journal of Database Management - ACM Digital Library
The Data Warehousing Institute: TDWI
International Journal of Data Warehousing (IJDW)
IFRSA International Journal of Data Warehousing & Mining
Journal of Data Mining - Microsoft Academic Search
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining, Modelling and Management

None

Develop Project Initiation documentation for a client Management information system, detailing your understanding of theory and practice.
Coursework: (1500 words) 50% Weighting

LO 1,2,3,4,5,6

Examination (1.5 hours) 50% Weighting

LO 1,2,3,4,5,6

To pass this module student must obtain overall 50% marks

Topics

Database Technologies and Systems
Relational database system,
Interfaces to relational database systems (e.g. Web databases, SQL, ASP/JSP Scripting languages).
Distributed Database Systems, and Client/Server Architectures.
Enterprise-wide database technologies (e.g. data warehouses, data mining).
Post-relational data models and database systems (e.g. Object-oriented database systems).
Interfaces to object database systems (e.g. Java, C++, etc¿).
Emerging database technologies (e.g. Active, Temporal, Spatial, and Multimedia Databases)

Data Warehousing
Review of database technology underpinning data warehousing and data mining.
Data warehouse logical design: star schemas, fact tables, dimensions, snowflake schemas, dimension hierarchies, data marts.
Data warehouse physical design: partitioning, parallelism, compression, indexes, materialized views.
Data warehouse construction: data extraction, transformation, loading and refreshing. Data warehouse support in Oracle. Warehouse metadata.

Data Mining
From data warehousing to data mining: OLAP architectures, OLAP operations. SQL extensions for OLAP.
Data mining approaches and applications. Data mining technologies and implementations. Techniques for mining large databases.
Data mining support in SQL Server, Oracle, Clementine. Data mining standards.
Research trends in data warehousing and data mining.

The learning strategy for the module requires students to commit 300 learning hours, of this there will be 75 hours of class support and 225 hours of independent and self directed study.

The lectures will be interactive with student participation in discussion about Database Technologies and Systems, Data Warehousing, and Data Mining addressing information distribution solutions and testing using simulation and case studies. Students will be allocated to small groups for various activities and workshop sessions. Tutorials are used to ensure that students understand the material and its relevance to the programme. Within the module, students will undertake individual presentations. These will be used to highlight understanding of module content, planning and implementation of ideas and innovations, self assessment and reflection, peer/tutor discussion and review.

For each topic, students will engage in preparation and reading prior to each seminar or practical workshop. Students will be asked to share their reading of the topic through discussion with their peer group, present their findings to the seminar group and provide feedback to other students