COMP37332: Data Integration and Analysis (2008-2009)
The aim of the course is to give students an awareness of the problems and opportunities associated with data integration, including the analysis of the data once integrated.
A student completing this course unit should:
Understand the main issues in data integration and analysis, and be aware of potential approaches (A).
Be aware of the principal challenges that have to be addressed in the development of distributed database systems (A).
Understand the key issues, advantages and problems in data integration and warehousing, including various architectures, models and designs (A, B).
Understand data analysis approaches, including OLAP and data mining, and be familiar with the data analysis process, its motivation, applicability, advantages and issues (A, B).
Be familiar with the principles and techniques for different data mining tasks, including data classification, clustering and association analysis (A, B).
Be able to identify application areas, opportunities and challenges in data integration and analysis tasks (B).
Assessment of Learning outcomesAll learning outcomes are assessed by examination and during lab sessions.
Examination: 85% (3 questions from 5).
Laboratory: 15% (5 sessions, practicing tools and methods discussed during the lectures).
Contribution to Programme Learning OutcomesA2, A5, B3.
Introduction to Data Integration and Analysis
A review of current technologies, the issues raised by them, and outstanding problems. (1)
Rationale; transparency; architectures; top-down and bottom-up distributed database design; Oracle as a case study. (5)
Data models and architectures for warehousing; ETL (extract, transform, load) process; data integration and meta-data generation. (3)
Online analytical processing (OLAP)
Introduction to OLAP; OLAP operations and SQL extensions; case studies; trends and open issues. (4)
Rationale, aims and approaches, KDD (knowledge discovery in databases); techniques and algorithms for association analysis, data classification and clustering; evaluation of data mining; application areas and case studies; major open issues. (9)
Core TextTitle: Principles of distributed database systems (3rd edition)
Author: Ozsu, M. Tamer and Patrick Valduriez
Supplementary TextTitle: Database systems: a practical approach to design, implementation, and management (4th edition)
Author: Connolly, Thomas and Carolyn Begg
Supplementary TextTitle: Fundamentals of database systems (5th edition)
Author: Elmasri, Ramez and Shamkanth B. Navathe