Units

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Sample subject outline - Semester 1 2013

Note: Subject outlines often change before the semester begins. Below is a sample outline.

Rationale

Successful companies operate their business efficiently. However, with the rapid growth of data and digital repositories, it is a big challenge for companies to stay competitive in today's market. To this end, there is an increasing awareness of benefits and potential competitive advantage that data warehousing and mining techniques can provide for Business Intelligence. Data warehousing and mining are relatively new terms although the concepts have been around us for years. Data warehousing represents an ideal vision of maintaining a central digital repository of all organizational data that can be smartly used through data mining tools to maximize business profits. Data warehousing is becoming more and more popular recently and recognized by IT industry as the dominating technique for applications of databases in the future.

This unit discusses the concepts, architectures and methods of data warehousing and mining techniques, e.g., data warehouse architecture and schema, data cubes and OLAP (on-line analytical processing), ETL (Data Extraction, Transformation and Loading) process, data quality and report, patterns and sequences mining, association analysis, and decision tables. It also focuses on the topics and techniques that are most promising for building and analyzing multidimensional data for efficiently organizing data warehouses and mining tools. Through this study, students will be able to demonstrate knowledge of data warehousing and mining, and skills of designing, developing and implementing data warehousing components in SQL environments. It also enables students to design systems and tools that provide services to data management and analysis, such as data warehousing systems, data mining and analysis tools, business intelligence based systems, smart information uses, and data processing systems.

You may take this unit if you are enrolled in other cources, or in other degree programs of the University, as long as you meet the required prerequisites or equivalents (the assumed knowledge).

Aims

This unit teaches the foundations of data warehousing and mining for producing systems that provide valuable services and decision support to busuness companies. Through this study, you will be able to demonstrate knowledge of the principles and techniques of data warehouse architecture and schema, OLAP and data cubes, ETL and data quality, patterns and sequences mining, association analysis, and decision tables. You will also be able to use and develop smart data services for business intelligence.

Objectives

On successful completion of this unit you should be able to:
Theory:
· Demonstrate knowledge of data warehouse architecture and schema (GC1);
· Demonstrate knowledge of OLAP and data cubes (GC1);
Demonstrate knowledge of ETL and data quality (GC1);
· Demonstrate understanding of patterns and sequences mining data clustering, and association analysis (GC1, GC2);
· Demonstrate understanding of decision tables (GC1, GC2);
Practice:
· Apply SQL environment to implement data warehouses, and data mining (GC1, GC2);
· Use Microsoft tools for knowledge discoovery, management and analysis (GC1, GC2);
· Show improvement in your critical, creative and analytical thinking and effective problem solving within the IT context through learning and applying a data warehousing system (GC1, GC2, GC3);
· Demonstrate the ability to work in a self-reliant and independent way including the ability to manage time and prioritise activities to achieve deadlines typical of work with data warehousing and mining (GC4); and
· Demonstrate an aptitude for lifelong learning and develop a sense of basic curiosity about aspects of information technology (GC4).


Key: FIT Graduate Capabilities
GC1 - Knowledge and Skills
GC2 - Critical and Creative Thinking
GC3 - Communication
GC4 - Lifelong Learning

Content

In this unit you will learn:
· SQL server introdcution;
· Data warehouse architecture and schema ;
·
· OLAP and Data cubes;
· ETL and data quality;
· Introduction to SQL data mining extension;
· Pattern and sequence mining;
· Association analysis;
· Data clustering;
· Decision tables;

Approaches to Teaching and Learning

Weekly contact is a 2-hour lecture and 1-hour practical. The Blackboard contains slides for lectures. However, although the slides contain the content of the unit they are insufficient. You are expected to attend lectures and take notes on anything the lecturer says which is not included in the slides. More detail can be found in the textbook. In lectures, where appropriate, examples will be worked out and code explained. The practicals contain some exercises relevant to lectures. You should study the lecture material, work out the exercises and check the answers against the solutions, to verify your understanding of the material. To clarify anything, check with any teachers or lecturer associated with this unit either by email or during their normal consulting times. This learning process requires your weekly commitment. The learning process addresses objectives 1 through 4, 8 and 9. Both assignments addresse objectives 5-9.

Practical: 1 hour weekly tasks. These will reinforce the practical and theory presented in the lecture. These commence in Week 2. Lab based classes for these tasks are conducted by teachers who will facilitate this vital practical experience. You may use the practical class to ask questions about your assignments.

Assessment

Criterion-Referenced Assessment
Appropriate assessment criteria will be made available to students at the introduction of each assignment.· You will receive feedback on completed and marked assignments via individual comments on assignment
· scripts.
· Teachers and the unit coordinator and lecturers will be available in person at specified times or via email to answer questions.
· For the Final Exam you are referred to the Faculty's formal Rules, Policy and Procedures.

Assessment name: Project (applied)
Description: Data warehousing
Relates to objectives: 5, 6, 7, 8, 9
Weight: 25%
Internal or external: Internal
Group or individual: Group with Individual Component
Due date: Week 8

Assessment name: Problem Solving Task
Description: SQL Data mining
Relates to objectives: 5, 6, 7, 8, 9
Weight: 25%
Internal or external: Internal
Group or individual: Group with Individual Component
Due date: Week 13

Assessment name: Examination (Theory)
Description: Testing Weeks 1-13 Lectures & Practicals
Relates to objectives: 1- 4, 7, 8, 9
Weight: 50%
Internal or external: Internal
Group or individual: Individual
Due date: Exam period

Academic Honesty

QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.

A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.

Resource materials

Recommanded Textbook(s):
· Silvers, Fon, ¿Building and maintaining a data warehouse¿, 2008, Auerbach , (ISBN: 9781420064629), Chapter 6, (QUT Library E-book).
MacLennan, Jamie; Tang, ZhaoHui; and Crivat, Bogdan (2009), Data Mining with Microsoft SQL Server 2008. Wiley Publishing, Inc., (ISBN: 987-470-27774-4) (QUT Library E-book).

Reference(s):
Malinowski, Elzbieta and Zimanyi, Esteban, ¿Advanced data warehouse design¿, Springer, 2008. Chapter 2 (QUT Library E-book)

J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2006, 2nd edition (QUT Library e-book).

Chris Leiter et al., Beginning Microsoft SQL Server 2008 Administration, Wrox, Hoboken. Chapter 16, (QUT Library E book)

G. Shmueli, N. R. Patel and P. C. Bruce, Data mining for business intelligence, John Wiley & Sons, 2007.

R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 5th Edition, Pearson, 2007.

Microsoft Data Mining Extensions (DMX) online reference:

No extraordinary charges or costs are associated with the requirements for this unit.

Risk assessment statement

There are no out of the ordinary risks associated with this unit.

Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.

Last modified: 24-Oct-2012