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
Unit convenor and teaching staff |
Unit convenor and teaching staff
Amin Beheshti
Contact via +61 (2) 9850 6344
Room 365, BD Building
By Appointment
Guanfeng Liu
Contact via +61-2-9850-9542
366, BD Building
By Appointment
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
130cp at 1000 level or above including COMP2200 or COMP257
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
Even simple tasks like counting elements can seem impossible when the amount of data to process is huge. This unit explores some of the key
aspects related to processing and mining information from large volumes of data. We present technology commonly used in industry such as map-reduce, and show how a range of data processing methods can be realised using map-reduce. Special emphasis will be placed in the adaptation of data mining techniques for large volumes of data and for data streaming.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
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
Information about important academic dates including deadlines for withdrawing from units are available at https://students.mq.edu.au/important-dates
General Assessment Information
All assignments will be submitted using iLearn. The results of all assignments will be available via iLearn.
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 solutions have been posted.
The final exam is a hurdle assessment. This means that:
The final mark of the unit will be obtained by summing the marks of all the assessment tasks for a total mark of 100. In order to pass the unit:
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
For details of days, times and rooms consult the timetables webpage.
Much of the contents of the unit will be based on the following books:
Additional material including lecture notes will be made available during the semester. See the unit schedule for a listing of the most relevant reading for each week.
The following software is used in COMP336:
This software is installed in the labs; you should also ensure that you have working copies of all the above on your own machine. Note that some of this software requires internet access.
Many packages come in various versions; to avoid potential incompatibilities, you should install versions as close as possible to those used in the labs.
The unit web page will be hosted in iLearn, where you will need to login 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.
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 1 - Data and Big Data
Week 2 - Organizing Big Data
Week 3 - Curating Big Data
Week 4 - Processing Big Data (Cloud Computing)
Week 5 - Processing Big Data (MapReduce)
Week 6 - Big Data Platforms (Guest Lecture - AWS/Microsoft/IBM)
Week 7: Big Data with High Dimensions
Week 8: Indexing Big Data
Week 9: Searching Big Data
Week 10: Multidimensional Divide and Conquer
Week 11: Grid Decomposition in Big Data
Week 12: Advanced Topic in Big Data (Guest Lecture)
Week 13: Unit and Exam Review
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:
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).
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 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
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
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.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
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
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.
The Big Data domain is advancing very fast. Accordingly, the content proposed in 2019 has been reviewed and updated for this offering. Particularly, we have offered new and trending topics in Big Data Platforms and Advanced Topic in Big Data.