Students
COMP8230 – Mining Unstructured Data
2020 – Session 1, Weekday attendance, North Ryde
Coronavirus (COVID-19) Update
Due to the Coronavirus (COVID-19) pandemic, any references to assessment tasks and on-campus delivery may no longer be up-to-date on this page.
Students should consult iLearn for revised unit information.
Find out more about the Coronavirus (COVID-19) and potential impacts on staff and students
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer
Amin Beheshti
Contact via +61-2-9850-6344
Room 365, BD Building
Unit Convenor
Guanfeng Liu
Contact via +61-2-9850-9541
Room 366, BD Building
Lecturer
Rolf Schwitter
Contact via +61-2-9850-9533
Room 359, BD Building
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|---|---|
| Credit points |
Credit points
10
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| Prerequisites |
Prerequisites
COMP6200 or ITEC657
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
The aim of this unit is to show where data warehouse and business intelligence technologies are at in this point in time so that business managers know what is possible for their next business strategy. As such this unit is primarily concerned with developing an awareness of what these technologies are currently capable of, rather than creating business intelligence developers. The unit will follow a typical lifecycle of a data warehouse/business intelligence project, involving the following broad phases: extraction transformation and loading data from source systems; building OLAP cubes - once the preserve of elite analysts, OLAP is quickly becoming a ubiquitous technology; data mining - once the preserve of the Fortune 100 companies, it is now a commodity technology available to all; and creating business intelligence tools.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- ULO1: Demonstrate an understanding of basic concepts, techniques, algorithms and modellings in unstructured data mining.
- ULO2: Identify the appropriate data mining techniques and algorithms for real life unstructured data mining problems.
- ULO3: Explain how good decision making is supported by descriptive and predictive data mining
- ULO4: Present and analyse the unstructured data mining results with advanced data mining techniques.
- ULO5: Communicate clearly and effectively
Assessment Tasks
Coronavirus (COVID-19) Update
Assessment details are no longer provided here as a result of changes due to the Coronavirus (COVID-19) pandemic.
Students should consult iLearn for revised unit information.
Find out more about the Coronavirus (COVID-19) and potential impacts on staff and students
General Assessment Information
Details for each assignment will be available via iLearn
You are encouraged to:
- set your personal deadline earlier than the actual one;
- keep backups of all your important files;
- ensure that no-one else picks up your printouts.
Late submission
No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total. No submission will be accepted after the solutions have been posted.
Delivery and Resources
Coronavirus (COVID-19) Update
Any references to on-campus delivery below may no longer be relevant due to COVID-19.
Please check here for updated delivery information: https://ask.mq.edu.au/account/pub/display/unit_status
Classes
Each week has two hours of lectures. Students need to attend and participate in at least 10 lectures in order to get full marks for class participation. For details of days, times and rooms consult the timetables webpage.
Note there is no workshop/practical for the class.
Required and Recommended Texts
All required and recommended readings will be provided as part of the lecture material.
Unit Web Page
The unit web page will be hosted in iLearn, where you will need to log in using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, they will be monitored by the staff on the unit.
Unit Schedule
Coronavirus (COVID-19) Update
The unit schedule/topics and any references to on-campus delivery below may no longer be relevant due to COVID-19. Please consult iLearn for latest details, and check here for updated delivery information: https://ask.mq.edu.au/account/pub/display/unit_status
| Week | TopicTeaching Staff | ||
|---|---|---|---|
| Big Data Curation: Turning Raw Data into Contextualise Data and Knowledge | |||
| Week 1 | Survey Data Curation from Cleaning to Adding Value | Amin Beheshti | |
| Week 2 | Cleaning | Amin Beheshti | |
| Week 3 | Contextualizing | Amin Beheshti | |
| Week 4 | Analyzing | Amin Beheshti | |
| Efficiently and Effectively Mining Contextual Networking Data | |||
| Week 5 | Context-Aware Trust Relation Mining in Social Networks | Guanfeng Liu | |
| Week 6 | Context-Aware Path Mining in Networking Data | Guanfeng Liu | |
| Week 7 | Graph Pattern Matching in Large-Scale Networking Data | Guanfeng Liu | |
| Week 8 | Social Network-Based Community Mining | Guanfeng Liu | |
| “Making Sense” Out of Unstructured Data Using Semantic Web Technologies | |||
| Week 9 | Making Sense out of Unstructured Data | Rolf Schwitter | |
| Week 10 | Validating RDF Graphs | Rolf Schwitter | |
| Week 11 | Ontology Engineering | Rolf Schwitter | |
| Week 12 | RuleML and Rule Languages | Rolf Schwitter | |
| Week 13 | The Applications of Mining Unstructured Data (Industry Talk) | Invited Speakers | |
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Grade Appeal Policy
- Complaint Management Procedure for Students and Members of the Public
- Special Consideration Policy (Note: The Special Consideration Policy is effective from 4 December 2017 and replaces the Disruption to Studies Policy.)
Students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
Results
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
The Library provides online and face to face support to help you find and use relevant information resources.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
If you are a Global MBA student contact globalmba.support@mq.edu.au
IT Help
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.