COMP33111: Data Integration and Analysis (2011-2012)
All application areas are witnessing the "data deluge", i.e. the ever growing amount of digital data available as part of day-to-day activities in business, science, education, entertainment, etc. Making sense of this data by integration and analysis is a key for success of any organisation. In addition to the need to work with huge volumes of data, current applications are also challenged with multi-modal data, including un- and semi-structured data, text, image and video data, spatial and temporal data, etc.
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. Previous database courses focused on the infrastructure for managing and querying data, database design and database programming. This course unit focuses principally on making the most of data within an organisation through
Data integration: getting the data into a form that supports and facilitates aggregation, exploration and mining.
Data analysis: techniques for learning new lessons from the data.
|Programme outcome||Unit learning outcomes||Assessment|
|A2 A5||Understand the main issues in data integration and analysis, and be aware of potential approaches.|
|A2 A5 B3||Understand the key issues, advantages and problems in data integration and warehousing, including various architectures, models and designs.|
|A2 A5 B3||Understand data analysis approaches, including OLAP and data mining, and be familiar with the data analysis process, its motivation, applicability, advantages and issues.|
|A2 A5 B3||Be familiar with the principles and techniques for different data mining tasks, including data classification, clustering and association analysis.|
|A2 A5 B3||Understand the main issues in multi-modal data, and be familiar with techniques for storage, querying and retrieval of multimedia data.|
|B3||Be able to identify application areas, opportunities and challenges in data integration and analysis tasks.|
Introduction to Data Integration and Analysis
A review of current technologies, the issues raised by them, and outstanding problems. (1)
Data models and architectures for warehousing; ETL (extract, transform, load) process; data quality, 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. (10)
Mining and integration of multi-modal data
Nature of multi-modal and multimedia data; content-based querying and retrieval; meta-data generation, ontologies, semantic annotation and integration; querying and retrieval from textual databases. (4)
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