Students

COMP6210 – Big Data

2020 – Session 2, Special circumstance

Notice

As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.

To check the availability of face to face 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.

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit convenor and lecturer
Yan Wang
yan.wang@mq.edu.au
Contact via +61-2-9850 9539
Room 354, BD Building
By Appointment
Lecturer and tutor
Guanfeng Liu
guanfeng.liu@mq.edu.au
Contact via +61-2-9850-9542
Room 366, BD Building
By Appointment
Tutor
Urvashi Khanna
urvashi.khanna@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
Admission to MDataSc or MScInnovationIT or GradCertInfoTech
Corequisites Corequisites
COMP6200 or ITEC657
Co-badged status Co-badged status
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.

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: Apply Map-reduce techniques to a number of problems that involve Big Data.
  • ULO2: Apply Big Data techniques to data mining.
  • ULO3: Explain the key Big Data concepts and techniques.
  • ULO4: Apply techniques for storing large volumes of data.

General Assessment Information

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

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.

Assessment Tasks

Name Weighting Hurdle Due
Assignment 1 20% No Week 8
Assignment 2 20% No Week 13
Final examination 60% No TBA

Assignment 1

Assessment Type 1: Practice-based task
Indicative Time on Task 2: 30 hours
Due: Week 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.

 
On successful completion you will be able to:
  • Apply Map-reduce techniques to a number of problems that involve Big Data.
  • Apply Big Data techniques to data mining.
  • Apply techniques for storing large volumes of data.

Assignment 2

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.

 
On successful completion you will be able to:
  • Apply Big Data techniques to data mining.
  • Explain the key Big Data concepts and techniques.

Final examination

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.

 
On successful completion you will be able to:
  • Apply Map-reduce techniques to a number of problems that involve Big Data.
  • Apply Big Data techniques to data mining.
  • Explain the key Big Data concepts and techniques.
  • Apply techniques for storing large volumes of data.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

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:

  • J. Leskovec, A. Rajaraman, J. Ullman, Mining of Massive Datasets. The book is free and available from http://www.mmds.org/, where you can also find links to a MOOC, slides, and videos.
  • C.Coronel, S. Morris. Database Systems: Design, Implementation and Management. 13th edition. Chapter 14 is the most relevant chapter. This chapter will be made available to students attending the classes.

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 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.

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.

Unit Schedule

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

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:

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.

Changes from Previous Offering

Compared to Semester 1, three assignments are reduced to two assignments. There is no hurdle any more.

Changes since First Published

Date Description
25/07/2020 There is no hurdle in S2 2020. But I found an error below which has been corrected. OLD VERSION: In order to pass the unit: • The sum of all assessed tasks must be at least 50. • The final mark of the exam must be at least 30 out of 60. NEW VERSION: In order to pass the unit, the raw mark needs to be 50 or above.