Assessment Brief- Assessment 3- Map-Reduce Programming Challenge
Unit Code/Description
ICT313 Big Data for Software Development
Course/Subject
Bachelor of Information Technology
Semester
S1 – 2025
Unit Learning Outcomes Addressed
ULO3: Critically assess and implement advanced data pre-processing and analytics strategies in a software development context, focusing on tasks like data cleansing, transformation, and feature selection.
ULO4: Design, develop, and evaluate big data solutions using programming models like Map-Reduce and technologies like Hadoop, tailored specifically to address software development needs such as DevOps integration and quality assurance.
Assessment Objective
The objective of this assessment is to assess student’s knowledge and practical skills in working with large-scale datasets and leveraging Hadoop ecosystem tools and technologies for data processing and analysis.
Assessment Title/Type
Assessment 3: Map-Reduce Programming Challenge (Individual Assignment)
Due Date
Week 10, Sunday, 18 May, 11.59 PM
Weighting
20%
Instructions to Students
See the assignment description in below
Format/Structure
Ms Word or PDF for the report, dataset and code files
Word/Page limit
length of 500 words for the report, font Calibri 12
Referencing Style
American Psychological Association (APA)
Submission Guidelines
•
All work must be submitted on Moodle by the due date
•
A PDF or Ms Word file must be submitted which includes all required steps, discussion and evidence of completion of tasks
•
Students must present a demo of the project to their lecturer in week 11, otherwise, they will receive no mark for their submission.
Plagiarism and Academic Integrity
At CIHE, we take academic integrity seriously and expect all students to maintain the highest standards of honesty and ethical behaviour in their academic work. As a student, it is your responsibility to ensure that all your academic endeavors are conducted with integrity and in accordance with the principles of honesty, fairness, and respect for intellectual property. Please refer to “CIHE Student Academic Integrity and Honesty Policy” in the Moodle for details.
Late Submission Policy
An assessment item submitted after the assessment due date, without an approved extension or without approved mitigating circumstances, will be penalised. The standard penalty is the reduction of the mark allocated to the assessment item by 10% of the total mark applicable for the assessment item, for each day or part day that the item is late. Assessment items submitted more than ten days after the assessment due date are awarded zero marks.
Assignment Description (Total marks 20)
Supporting Materials
All supporting materials for this assessment can be found in Hadoop Files folder in Moodle:
1
- A virtual machine has been prepared for you on which Ubuntu and Hadoop have been installed and configured (Hadoop Virtual Machine). All files related to the virtual machine can be found in the zip file Hadoop_VM (WMWare) or Hadoop_VM (VirtualBox). You need to download the Zip file and open it on your computer’s hard drive. Then, you need to install VMWare Player on your computer and open the virtual machine file.
2 - Virtual Machine Tutorial (Part 1 and 2) is a tutorial video on how to use the virtual machine. It shows step by step on how you can you start Hadoop and run a WordCount example.
3 - Hadoop Tutorial.PDF also provides you with detailed instructions on how to start Hadoop and run WordCount example.
Instructions
The following file contains user ratings for Amazon products:
Amazon Product Review
(Note: If the link doesn’t work, you can download the file from Moodle. It exists in the Assessment section).
Each user has rated at least one product. The format of the data file is CSV and contains four columns: User ID, Product ID, Rating, Timestamp. Rating is from 1 to 5. The timestamps are unix seconds since 1/1/1970 UTC. For example, the following line of the file
A000681618A3WRMCK53V B0002Y5WZM 2 1383609600
Is interpreted as follows: User A000681618A3WRMCK53V has rated product B0002Y5WZM, 2/5 at time 1383609600 (Tuesday, Nov 05 2013 11:00:00, Australian Eastern Daylight Time).
Your task is to use MapReduce programming and find the average rating for each product. Here is an example of the output:
Product ID
Average Rating
0321732944
3.47
0439886341
4.21
You can choose the output format. However, the required information must be included in the output. You need to include the output file in your submission.
Deliverable
You need to submit an MS Word or a PDF file which includes the following items:
The source code for map and reduce function (copied/pasted into the MS Word or PDF file; no separate file is needed).
The output file.
Enough screenshots on the steps taken to get the program running.
Screenshots for the output generated by the program. The student’s name must be also part of the printed information. Annotate all screenshots with brief descriptions (one line or two is enough).
In all screenshots, the date and time of the computer must be clearly shown in the corner (look at the sample below). Make sure the date and time of your computer is correct.
A section for discussing the potential benefits of your project for Amazon. You need to explain how Amazon can make informed decisions based on the results of your project.
This section must be 450 – 550 words.
Note: In order to receive a mark for your submission, it is mandatory to present a demo of your project to your lecturer during week 12. Failure to do so will result in a zero mark for your submission. The lecturer has the discretion to adjust team contributions based on individual contributions to the demo. It is important to demonstrate your active participation and contribution to the project during the demo to ensure fair grading and assessment.
Marking Rubric
Criteria Poor (0-25%) Fair (25-50%) Good (50-75%) Excellent (75-100%)
Data Preparation
(3 marks)
No evidence to show
Hadoop runs correctly
Hadoop is up and running
The data file is not
downloaded correctly
Hadoop is up and running
The data file is downloaded correctly
Hadoop is up and running
The data file is downloaded and put in HDFS
Map and Reduce
Functions
(5 marks)
No Map or Reduce function
is included
Either Map or Reduce function is implemented correctly
Map and Reduce functions are implemented but there are minor issues
Map and Reduce functions are implemented correctly and included in the report
Screenshots of the whole
process
(3 marks)
No screenshot or not related screenshots to the process of data preparation, running Hadoop and MapReduce programming
Only few screenshots are included Several steps are missing or have no evidence No or poor annotations
Only one or two steps are missing or have no evidence Annotations for screenshots are not comprehensive
Enough screenshots are included to show the whole process is correct: data preparation, running Hadoop and MapReduce programming.
Screenshots are well annotated
Output
(4 marks)
The output is incorrect
No team member’s name is shown on the screenshot
Some parts of the output are
correct
Team members’ names are shown on the screenshot
The output is correct but no team member’s name is shown on the screenshot
The output is correct and all required
information is included
Screenshots as evidence attached
Names of group members are shown on the screenshots
Discussion
(3 marks)
Discussion is vague or very brief lacks of details and reasoning
Discussion covers some potential benefits of the project for Amazon.
Discussion covers several potential benefits of the project for Amazon.
Length of this section is 500 words
An insightful discussion has been provided that covers the potential benefits of the project for Amazon.
Length of this section is 500 words
Language
(2 marks)
The report is badly structured and written, containing numerous grammatical and spelling errors. The language is often confusing and inappropriate for the intended audience.
The report structure and writing need improvements.
It contains some grammatical and spelling errors. The language is sometimes imprecise or inappropriate for the intended audience.
The report is adequately structured, clearly written, and mostly free of grammatical and spelling errors. The language is appropriate for the intended audience.
The report is well-structured, clearly written, and free of grammatical and spelling errors. The language is sophisticated, precise, and appropriate for the intended audience.
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