Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit convenor and lecturer
Yan Wang
Contact via +61-2-9850 9539
Room 354, BD Building
By Appointment
Lecturer
Guanfeng Liu
Contact via +61-2-9850-9542
Room 366, BD Building
By Appointment
Tutor
Urvashi Khanna
Tutor
Asim Adnan Eija
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
COMP6200 and Admission to MDataSc or MScInnovationIT or GradCertInfoTech or MBusAnalytics
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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. Especial emphasis will be placed in the adaptation of data mining techniques for large volumes of data and for data streaming. |
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:
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 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, the raw mark needs to be 50 or above.
Name | Weighting | Hurdle | Due |
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Assignment 1 | 20% | No | Week 7-8 |
Assignment 2 | 20% | No | Week 13 |
Final examination | 60% | No | TBA |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 30 hours
Due: Week 7-8
Weighting: 20%
In this assignment you will implement MapReduce techniques for the processing of Big Data. You will build your assignment on top of Hadoop.
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 30 hours
Due: Week 13
Weighting: 20%
In this assignment you will implement a non-trivial problem that processes Big Data.
Assessment Type 1: Examination
Indicative Time on Task 2: 15 hours
Due: TBA
Weighting: 60%
The final exam will focus on the theoretical aspects of the unit, including algorithms and implementation issues.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
For details of days, times and rooms consult the timetables webpage.
Some 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 COMP3210/6210:
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.
Note: Lectures will be online.
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)
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 Review
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/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.
Compared to Semester 1 2020, three assignments are reduced to two assignments. There is no hurdle any more.
Unit information based on version 2021.04 of the Handbook