CAB430 Data and Information Integration
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | CAB430 |
|---|---|
| Prerequisite(s): | CAB220 or DSB100 or CAB201 or ITD121 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $4,104 |
| International unit fee | $4,788 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | CAB430 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB220 or DSB100 or CAB201 or ITD121) or Admission to EN60 |
| Assumed Knowledge: | CAB201 Programming Principles or equivalent. |
| Coordinator: | Yue Xu | yue.xu@qut.edu.au |
Overview
With the rapid growth of data and digital repositories, there is an increasing awareness of benefits of data warehousing and mining techniques for Business Intelligence. 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 recognized by the IT industry as a dominant 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, association analysis and classification. 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.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain data warehouse concepts and processes of ETL (Data Extraction, Transformation and Loading) ) and OLAP (on-line analytical processing)
- Explain the concepts and algorithms of pattern and association mining, data clustering and classification
- Use Microsoft tools to build data warehouses from multiple datasets
- Apply data mining algorithms to analyse the data in data warehouses to answer users' queries for decision making
- Work in a self-reliant and independent way including the ability to manage time and prioritise activities to achieve deadlines.
- Write a professional report to justify the approach taken to build the data warehouse and data analysis
Content
The following topics will be covered:
- Introduction to SQL server
- 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
- Data classification
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend on average between 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
Each week pre-recorded lectures will be available to introduce the core material and provide the theoretical basis of the subject. Weekly practicals held on-campus or online, 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 the lecturer or tutors either by email or in class. This learning process requires your weekly commitment.
Practicals commence in Week 2. The practicals will reinforce the practical and theory presented in the pre-recorded lecture. The practicals are designed with a focus on development of practical skills. You may use the practical class to ask questions about your assignments.
Feedback on Learning and Assessment
- You will receive feedback on completed and marked assignments via individual comments on assignment scripts.
- The teaching staff will be available in person at specified times or via email to answer questions.
- You can ask the teaching staff for advice and assistance during lectures and practical sessions.
- For the Final Exam you are referred to the Faculty's formal Rules, Policy and Procedures.
Assessment
Overview
Appropriate assessment criteria will be made available to students at the introduction of each assignment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (applied)
Data warehousing.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
SQL Data mining
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Timed Online Assessment
Three (3) hour duration
Testing Weeks 1-13 Lectures & Practicals.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit.
Resource Materials
Recommended text(s)
J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2011, 3nd edition (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).
Silvers, Fon, Building and maintaining a data warehouse, 2008, Auerbach , (ISBN: 9781420064629), (QUT Library E-book).
Reference book(s)
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.
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).
Malinowski, Elzbieta and Zimanyi, Esteban, Advanced data warehouse design, Springer, 2008. Chapter 2 (QUT Library E-book)
R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 5th Edition, Pearson, 2007.
Other
Microsoft Data Mining Extensions (DMX) online reference
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN60 Graduate Certificate in Communication for Engineering
- Demonstrate and apply specialised knowledge and technical skills in at least one Engineering discipline.
Relates to: Project (applied), Project (applied), Timed Online Assessment - Critically investigate real world engineering issues and solve complex problems drawing on specialised creative skills, analysis, evaluation and synthesis of discipline knowledge, theory and practice.
Relates to: Project (applied), Timed Online Assessment - Employ effective written and oral professional communication skills across social, cultural and discipline domains.
Relates to: Project (applied), Project (applied) - Exercise responsibility and accountability in applying knowledge and skills for own learning and effective practice including working independently, ethically and collaboratively.
Relates to: Project (applied)
Unit Outline: Semester 1 2024, Online
| Unit code: | CAB430 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB220 or DSB100 or CAB201 or ITD121) or Admission to EN60 |
| Assumed Knowledge: | CAB201 Programming Principles or equivalent. |
Overview
With the rapid growth of data and digital repositories, there is an increasing awareness of benefits of data warehousing and mining techniques for Business Intelligence. 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 recognized by the IT industry as a dominant 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, association analysis and classification. 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.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain data warehouse concepts and processes of ETL (Data Extraction, Transformation and Loading) ) and OLAP (on-line analytical processing)
- Explain the concepts and algorithms of pattern and association mining, data clustering and classification
- Use Microsoft tools to build data warehouses from multiple datasets
- Apply data mining algorithms to analyse the data in data warehouses to answer users' queries for decision making
- Work in a self-reliant and independent way including the ability to manage time and prioritise activities to achieve deadlines.
- Write a professional report to justify the approach taken to build the data warehouse and data analysis
Content
The following topics will be covered:
- Introduction to SQL server
- 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
- Data classification
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend on average between 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
Each week pre-recorded lectures will be available to introduce the core material and provide the theoretical basis of the subject. Weekly practicals held on-campus or online, 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 the lecturer or tutors either by email or in class. This learning process requires your weekly commitment.
Practicals commence in Week 2. The practicals will reinforce the practical and theory presented in the pre-recorded lecture. The practicals are designed with a focus on development of practical skills. You may use the practical class to ask questions about your assignments.
Feedback on Learning and Assessment
- You will receive feedback on completed and marked assignments via individual comments on assignment scripts.
- The teaching staff will be available in person at specified times or via email to answer questions.
- You can ask the teaching staff for advice and assistance during lectures and practical sessions.
- For the Final Exam you are referred to the Faculty's formal Rules, Policy and Procedures.
Assessment
Overview
Appropriate assessment criteria will be made available to students at the introduction of each assignment.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Project (applied)
Data warehousing.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
SQL Data mining
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Timed Online Assessment
Three (3) hour duration
Testing Weeks 1-13 Lectures & Practicals.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit.
Resource Materials
Recommended text(s)
J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2011, 3nd edition (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).
Silvers, Fon, Building and maintaining a data warehouse, 2008, Auerbach , (ISBN: 9781420064629), (QUT Library E-book).
Reference book(s)
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.
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).
Malinowski, Elzbieta and Zimanyi, Esteban, Advanced data warehouse design, Springer, 2008. Chapter 2 (QUT Library E-book)
R. Elmasri and S. B. Navathe, Fundamentals of Database Systems, 5th Edition, Pearson, 2007.
Other
Microsoft Data Mining Extensions (DMX) online reference
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN60 Graduate Certificate in Communication for Engineering
- Demonstrate and apply specialised knowledge and technical skills in at least one Engineering discipline.
Relates to: Project (applied), Project (applied), Timed Online Assessment - Critically investigate real world engineering issues and solve complex problems drawing on specialised creative skills, analysis, evaluation and synthesis of discipline knowledge, theory and practice.
Relates to: Project (applied), Timed Online Assessment - Employ effective written and oral professional communication skills across social, cultural and discipline domains.
Relates to: Project (applied), Project (applied) - Exercise responsibility and accountability in applying knowledge and skills for own learning and effective practice including working independently, ethically and collaboratively.
Relates to: Project (applied)