COMP33111: Data Integration and Analysis :: Additional Course Information

COMP33111
Data Integration and Analysis

Course Information (2011/12)

Lecturer: Goran Nenadic

Demonstrators: Azad Dehghan, Rosyzie Anna Apong, James Naish

Introduction

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.

Module Aims

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.

Official module description and syllabus

Schedule of lectures, tutorials and labs

All lectures are on Mondays 13:00-15:00 in Kilburn LF 15
Tutorials, open surgeries, labs: Mondays 12:00-13:00 (3rd year lab) and/or 15:00-16:00 (G23)

Week Date Lecture/Tutorial/Lab/Test

1 26/09
   Introduction to Data Integration and Analysis

Lecture 1: Data Integration and Analysis (slides)

Tutorial 1: Basic Data Exploration

data files:
products.txt; Fall2001.txt; bakarydata.csv; Brain size data; Wine data

Download and install Weka 3

Data mining with WEKA - slides

2 03/10
   Data Warehousing and Profiling

Lecture 2: Introduction to Data Warehousing, Profiling and Cleansing (slides)

Tutorial 2: Data warehousing, cleansing and profiling

data files:
labor.arff; contact-lenses.arff; Catalog_Orders.txt; Products.txt Web_orders.txt;
guide answers

E. Rahm, H. H. Do: Data Cleaning: Problems and Current Approaches, IEEE Data Engineering Bulletin
M. A. Shahzad: Data warehousing with ORACLE

3 10/10
   Data Analytics and Online Analytical Processing

Lecture 3: Introduction to data analytics and OLAP (slides)

Tutorial 3: Understanding OLAP functionalities and services

guide answers

OLAP demos: Dundas OLAP services and Radar Cube OLAP demos
OLAP with ORACLE

4 17/10
   Association Rule Mining

Lecture 4: Association rule mining

Tutorial 4: Association rule mining with WEKA

data files:
contact-lenses.arff; zoo.arff;
guide answers

E. Frank et al: Weka - a Machine Learning Workbench for Data Mining, Data Mining and Knowledge Discovery Handbook, Springer Verlag
Viveros M, Nearhos J, Rothman M: Applying Data Mining Techniques to a Health Insurance Information System, 22nd VLDB Conference 1996
Association rules with ORACLE

Prepare for Lab test 1

5 24/10
   Lab test 1 (DW, OLAP) and Text Mining

Lecture 5: Analysis of textual data (text mining)

Tutorial 5: Text mining

TerMine, GETM
guide answers

A. Hotho et al: A brief survey of text mining
Text mining with ORACLE

Lab test 1: data exploration, warehousing and analytics

6 Reading week

7 07/11
   Data Classification

Lecture 6: Data classification

Tutorial 6: Classification in WEKA

data files:
labor.arff; glass.arff; glass-minusatt.arff; vehicle.arff;
guide answers

Classification in ORACLE

8 14/11
   Data Clustering

Lecture 7: Data clustering

Tutorial 7: Clustering in WEKA

data files:
weather.arff; iris.arff; bank.arff; flagdata.arff;
guide answers

Clustering in ORACLE

9 21/11
   Multimedia Data Analysis and Integration

Lecture 8: Analysis and Integration of Multimedia Data

Tutorial 8: Multimedia

Calif and Emir: software and demos
PubMed/MEDLINE; MeSH browser
guide answers

Multimedia data management in ORACLE

Prepare for Lab test 2

10 28/11
   Lab test 2: Data Mining

11 05/12
   Enterprise Resource Planning

Lecture 9: Enterprise Resource Planning (ERP)

SAP case study: Boulanger

12 12/12
   Guest lecture

IBM Guest lecture: "Smart Analytics for Policing -
Data Warehousing for real applications"

Assessment

Examination: 85% (3 questions from 5).
Laboratory: 15% (2 test sessions, practicing tools and methods discussed during the lectures).

Past exam papers

Past exam papers are available here and here. Feedback on past papers is available here.
General information about Examination is here.

Lab-test sessions

There are 2 assessed lab sessions:

Lab-test 1 on Data warehousing and OLAP (worth 5% of the total mark) - October 24th, 14:00-15:00
Lab-test 2 on Data Mining (worth 10% of the total mark) - November 28th, 13:00-15:00.

Tutorials, labs and open surgeries

There are weekly sessions with tutorials, labs and open surgeries. Tasks and questions will be delivered in advance and will be discussed during tutorial sessions on Mondays 12:00-13:00 and 15:00-16:00 (every Monday from week 2, apart from weeks when the lab tests take part).

Supplementary textbooks

Thomas Connolly and Carolyn Begg: Database systems: a practical approach to design, implementation, and management, Addison-Wesley, 4th edition, 2005, ISBN: 0321210255
Ramez Elmasri and Shamkant B. Navathe: Fundamentals of database systems, Pearson, 5th edition, 2007, ISBN: 032141506X
Robert Nisbet, John Elder and Gary Miner Handbook of Statistical Analysis and Data Mining Applications, Elsevier, ISBN: 978-0-12-374765-5 (e-book)
Oded Maimon and Lior Rokach (Eds.): Data Mining and Knowledge Discovery Handbook, Springer, 2nd ed., 2010, ISBN: 978-0-387-09822-7 (e-book)