Unit Outline

In recent years, due to advancement of internet technologies and instrumentation of every part of our life, we have noticed a huge surge in data available to us. This revolution is termed as Big Data. This Big Data cannot be processed or managed by any traditional methods of processing. This has led to development of several high performance and distributed computing platforms and programming frameworks. The design of such platforms relies on distributed computing concepts which are implemented in the form of systems such as Clusters and Clouds, and Big Data frameworks such as MapReduce and Stream Computing. These systems play an important role in todays' research, academia or industries by providing the processing of data generated from a variety of networked resources, e.g. large data stores and information repositories, expensive instruments, social media, sensors networks, and multimedia services for a wide range of applications. The aim of this unit is to provide students with the foundation knowledge and understanding of Big Data and distributed computing systems and applications especially in context of Cloud. In other words, this unit will equip students with essential knowledge that is needed for building next-generation applications that are scalable and efficient and can process Big Data. Key topics that will be covered: parallel and distributed systems basics, Big Data platforms and programming and Cloud computing. The unit will also explain how the business models of enterprises are changing with these forms of computing that provide large storage and computation space without purchasing expensive computer systems.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1.	Analyse the problems and challenges associated with building distributed computing applications;
2.	Adapt the emerging big data and cloud technologies to support business applications.
3.	Design high performance and cloud applications to support scalable online services.
4.	Design big data processing applications to efficiently process high volume and velocity data

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KIT107

Alterations as a result of student feedback

More tutorial tasks will be introduced so that students get more practice.

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Lecture (On Campus)	A real-time (i.e. synchronous) interactive activity involving the whole class whose primary purpose is the presentation and structuring of information/ideas/skills to facilitate student learning. All students are expected to attend.	2	Weekly
On Campus	Computer Laboratory	A structured real-time (i.e. synchronous) computer-based activity in a small-group setting where the primary purpose is the clarification, exploration or reinforcement of subject content presented or accessed at another time or place (e.g. lecture, preparatory work). It is teacher supported and may involve student-teacher and/or student-student interaction and dialogue for achievement of its learning outcomes. The students enrolled in the class are expected to attend.	3	Weekly

Attendance / engagement expectations

If your unit is offered On campus, it is expected that you will attend all on-campus and onsite learning activities. This is to support your own learning and the development of a learning community within the unit. If you are unable to attend regularly, please discuss the situation with your course coordinator and/or our UConnect support team.

If your unit is offered Online, it is expected you will engage in all those activities as indicated in the Unit Outline, including any self-directed learning.

If you miss a learning activity for a legitimate reason (e.g., illness, carer responsibilities) teaching staff will attempt to provide alternative activities (e.g., make up readings) where it is possible.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Tutorial work	See the MyLO site for the due date	30 %	LO1, LO2, LO3, LO4
Assessment Task 2:	Big Data Assignment	Week 7	15 %	LO1, LO2, LO3
Assessment Task 3:	Cloud and Big Data Based Processing System	Week 13	20 %	LO1, LO2, LO3, LO4
Assessment Task 4:	Test	Week 14	35 %	LO1, LO2, LO3, LO4

Assessment details

Assessment Task 1: Tutorial work

Task Description:

Each tutorial will assessed based on completion of the tutorial task based on lecture and tutorial concepts taught. This tutorial task may involve solving a short problem, completing tutorial work or quiz.

Task Length:

not applicable

Due Date:

See the MyLO site for the due date

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply Big Data and Cloud Technologies	LO2
2	Analyse issues in designing high performing Big Data and Cloud applications	LO1
3	Implement simple big data and cloud applications based on business scenario	LO3, LO4
4	Configure and deploy real big data and cloud technologies according to business scenario	LO1, LO2

Assessment Task 2: Big Data Assignment

Task Description:

Students need to utilise Big Data platform to solve a big data problem. Detailed description will be posted on Mylo. Students need to submit their code and a video showing demonstration of the program

Task Length:

Software application (15 hours)

Due Date:

Week 7

Weight:

15 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Analyse the performance of big data application.	LO1
2	Implement a working big data application according to the business requirements	LO3
3	Identify the performance bottlenecks in big data application	LO1, LO2

Assessment Task 3: Cloud and Big Data Based Processing System

Task Description:

Detailed requirements will be provided on Mylo. Students may need to give a presentation on their work in the last lecture of the semester.
Students may want to do this assignment in groups. However, it is not compulsory to do this in groups. If done in groups, students will be interviewed about their full understanding of the assignment. Marks will be awarded based on the interview and their contribution. To assess the contribution on the submitted work, demos and interviews will be conducted during last week of the semester.

Task Length:

Software Application (approx. 30 hours)

Due Date:

Week 13

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Evaluate and analyse the performance issues of distributed computing application	LO1, LO2
2	Implement big data and cloud computing application	LO3, LO4
3	Develop solutions to tackle performance issue in distributed computing application	LO1, LO2, LO3, LO4

Assessment Task 4: Test

Task Description:

This test will evaluate the theoretical and conceptual understanding in solving and analyzing distributed computing application.

Task Length:

1.30 hours

Due Date:

Week 14

Weight:

35 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Ability to compute performance of a given distributed computing application	LO1
2	Analyse the requirements of big data and cloud computing technologies in context of business applications	LO2
3	Ability to design big data and cloud application	LO3, LO4
4	Ability to analyse the performance issues with a given application and suggest an appropriate solution	LO1

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.