Unit Information Management System

Data Warehousing (CITS3401, SEM-1, 2014, Crawley)



Faculty of Engineering, Computing & Mathematics


Computer Science & Software Engineering




Unit Outline




Data Warehousing


CITS3401


SEM-1, 2014


Campus: Crawley


Unit Coordinator: Professor Amitava Datta


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Unit details

Unit title Data Warehousing
Unit code CITS3401 Credit points 6
Availability SEM-1, 2014 (24/02/2014 - 21/06/2014)
Location Crawley Mode Face to face

Contact details

Faculty Faculty of Engineering, Computing & Mathematics
School Computer Science & Software Engineering
School website http://web.csse.uwa.edu.au/
Unit coordinator Professor Amitava Datta
Email amitava.datta@uwa.edu.au
Telephone 6488 3449
Consultation hours 10-11 AM Fridays
Lecturers
NamePositionEmailTelephone Number
Amitava DattaProfessoramitava.datta@uwa.edu.au6488 3449
Tutors/ Demonstrators/ Facilitators

Mr. Furqan Zahid

Unit contact hours

Lectures: 2 hrs per week;  labs: 2 hrs per week

Online handbook http://handbooks.uwa.edu.au/units/unitdetails?code=CITS3401
Unit website http://undergraduate.csse.uwa.edu.au/units/CITS3401/

Unit rules

Prerequisites CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases) or CITS2232 Databases; for pre-2012 courses: CITS1402 Relational Database Management Systems (formerly CITS1402 Introduction to Databases) or CITS2232 Databases
Corequisites
Advisable prior study
Incompatibility CITS4243 Advanced Databases
Approved quota

Unit description

Relational databases are the backbones of modern businesses in processing transactions and storing customer data. Most organisations usually deploy several relational databases for operational convenience. It is quite often necessary to integrate the information existing in different relational databases for planning and decision making. Data warehouses are built to facilitate planning and decision making in businesses integrating data from different relational databases. Online analytical processing (OLAP) is a technology that uses a data warehouse for answering aggregation queries often used in planning. While relational databases hold important transactional information of a business, the success of a business quite often depends on advanced planning and development of strategies based on customer behaviour. Data mining technologies are used for discovering such patterns and trends in data stored in relational databases. This unit introduces the key mechanisms in data warehousing, OLAP and data mining. It discusses logical and physical design of data warehouses including star schema, snowflake schema, data marts, partitioning and materialised views. Students study the use of data warehouses through a study of the OLAP technology including the multidimensional OLAP (MOLAP) and relational OLAP (ROLAP) architectures, OLAP operations and structured query language (SQL) support for OLAP. They learn modern data mining methods including clustering, association rule mining and machine learning techniques.

Learning outcomes

Students are able to (1) understand that discovering and extracting knowledge from a massive amount of data is a key problem in many scientific and business disciplines; (2) demonstrate a thorough understanding of key data mining and knowledge discovery principles and techniques; and (3) apply key data exploration and mining techniques in their chosen disciplines.

Unit structure

(See Timetable)

Unit schedule

Teaching and learning responsibilities

Teaching and learning strategies

Charter of student rights and responsibilities

Student Guild contact details

Uses of student feedback

ACE/AISE/CARS

Information for students with disabilities

Assessment

Assessment overview

Typically this unit is assessed in the following way(s): (1) laboratory-based exercises; (2) project work; and (3) a final examination. Further information is available in the unit outline.

Assessment mechanism

#ComponentWeightDue DateRelates To Outcomes
1.Project 125%April 26, 2013Competency in data warehousing and OLAP
2.Project 225%May 31, 2013Competency in various data mining techniques
3.Final examination50%June 2013

Assessment items

Item TitleDescriptionSubmission Procedure for Assignments
Project 1Project on Data Warehousing and OLAPcssubmit
Project 2Project on Data Miningcssubmit
Final examinationFinal examinationExamination venue

Academic literacy and academic misconduct

Appeals against academic assessment

Textbooks and resources

Recommended texts

Text

Han, J. and Kamber, M. Data Mining: Concepts and Techniques, 2nd edn: Elsevier/Morgan Kaufmann 2006

Suggested alternate texts

Additional texts

Technical requirements

Software requirements

Additional resources and reading

Other important information