Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor/Lecturer
Guanfeng Liu
Lecturer
Amin Beheshti
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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:
Important Academic Dates
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
Late submissions will be accepted but will incur a penalty unless there is an approved Special Consideration request. A 12-hour grace period will be given after which the following deductions will be applied to the awarded assessment mark: 12 to 24 hours late = 10% deduction; for each day thereafter, an additional 10% per day or part thereof will be applied until five days beyond the due date. After this time, a mark of zero (0) will be given. For example, an assessment worth 20% is due 5 pm on 1 January. Student A submits the assessment at 1 pm, 3 January. The assessment received a mark of 15/20. A 20% deduction is then applied to the mark of 15, resulting in the loss of three (3) marks. Student A is then awarded a final mark of 12/20.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Assignment 1 | 20% | No | Week 7 |
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
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.
Required and Recommended Texts
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.
Technology Used and Required
The following software is used in COMP6210:
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.
Unit Web Page
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.
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
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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.
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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
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
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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.
Date | Description |
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07/02/2022 | Based on Gaurav's suggestion to resubmit it. |
Unit information based on version 2022.04 of the Handbook