Module Title:   Advanced Database Techniques

Module Credit:   20

Module Code:   CM-0423D

Academic Year:   2015/6

Teaching Period:   Semester 2

Module Occurrence:   A

Module Level:   FHEQ Level 7

Module Type:   Standard module

Provider:   Computer Science

Related Department/Subject Area:   SCIM (Dept of Computer Science)

Principal Co-ordinator:   Professor Daniel Neagu, Mr. M. J. Ridley

Additional Tutor(s):   -

Prerequisite(s):   None

Corequisite(s):   None

Aims:
To develop a thorough understanding of the theory and practice of advanced database techniques. This will include data storage and data manipulation issues, advanced data schema issues and data analysis, data processing and data mining issues.

Learning Teaching & Assessment Strategy:
A combination of lectures/tutorials/lab sessions/directed study. Concepts, principles & theories explored in formal lectures, practised & demonstrated in laboratory classes and practised and discussed in tutorials. Practical skills developed in laboratory sessions. Oral feedback is given during labs & tutorials. Coursework will assess the application of practical skills to the knowledge base of the module; the examination will assess the wider learning outcomes expressed in the descriptor. The coursework report should provide a state-of-the-art overview in a selected topic, critical analysis and evaluation of advanced database techniques and data mining applied to a case study.
Supplementary assessment is to repair deficiencies in the original submissions.

Lectures:   24.00          Directed Study:   144.00           
Seminars/Tutorials:   6.00          Other:   0.00           
Laboratory/Practical:   24.00          Formal Exams:   2.00          Total:   200.00

On successful completion of this module you will be able to...

critically analyse and evaluate the theory and practice of advanced database techniques and data mining approaches.

On successful completion of this module you will be able to...

apply skills in the design and implementation of database solutions using advanced database techniques and data mining approaches in specific case studies.

On successful completion of this module you will be able to...

critically analyse, evaluate and present in writing

  Coursework   60%
 
  Report (2000 words or equivalent): state-of-the-art overview in a selected topic, critical analysis and evaluation of a
  Examination - closed book 2.00 40%
 
  Examination

Outline Syllabus:
Optimization of Query Processing. Benchmarking. Adaptive Query Processing. Some case studies may include object-relational or object-and-XML systems. Data Mining. Data, Information and Knowledge. Data Preparation (Statistical Evaluation, Data Selection, Data Cleaning, Transformation of Data). Data Mining Algorithms (Association, Classification, Clustering, Time Series Analysis). Data Mining Methodologies.
Using Data Mining in the analysis of scientific data.

Version No:  2