Updated August 2018 1 of 3 TABLE OF CONTENTS
Main Objective of the assessment........................................................................................................................ 1
Description of the Assessment.............................................................................................................................. 1
Learning Outcomes and Marking Criteria ............................................................................................................. 1
Format of the Assessment .................................................................................................................................... 2
Submission Instructions ........................................................................................................................................ 3
Avoiding Plagiarism............................................................................................................................................... 3
Late Coursework ................................................................................................................................................... 3
Assessment Title Individual Project Development
Module Leader Allan Tucker
Distribution Date Week 4
Submission Deadline 6 January 2020
Feedback by 15 working days after submission
Contribution to overall module assessment 100%
Indicative student time working on assessment 42 Hours
Word or Page Limit (if applicable)
There is no word/page limit for this assessment, but
the best effort should be made to ensure the
submission is as concise as possible.
Assessment Type (individual or group) Individual
MAIN OBJECTIVE OF THE ASSESSMENT
In this assessment, you are required to demonstrate the appropriate practical skills and abilities to implement
solutions using modern large-scale data storage and processing infrastructures, and to critically reflect on the
concepts, theory and use of high performance computational infrastructures.
DESCRIPTION OF THE ASSESSMENT
You are required to identify and analyse a real-world problem, design and implement a solution to the
problem using Hadoop, and evaluate your implementation. The problem can be a simplified version from its
original scale, extent or level of difficulties etc. An indicative list of sample problems have been provided at
the end of this document. You may choose one of the problems in the list, but you are encouraged to identify
your own problem for the project.
The assessment has two weighted components:
1. Oral presentation (PASS / FAIL). A workshop will be held near the end of the term. Each candidate will be
allocated with 10 minutes (including question time) to present their individual project development and
demonstrate, if any, your prototype software. You should take this as an opportunity to seek feedback and
improve your project for the final submission.
2. Report (100%). A written report including the theory behind and the development of the individual project
needs to be submitted.
LEARNING OUTCOMES AND MARKING CRITERIA
Learning Outcomes:
LO1: Demonstrate the appropriate practical skills/abilities required to implement solutions using modern
large-scale data storage and processing infrastructures.
CS5607 High Performance Computational
Infrastructures
Assessment/Coursework for 2018/19
Department of Computer Science
Updated August 2018 2 of 3
LO2: Reflect critically on the concepts, theory and appropriate use of large-scale data storage and processing
infrastructures (commonly used in modern organisational environments).
Marking Criteria:
The coursework will be marked for 4 main criteria:
1. Demonstrating an understanding of the relevant theory underpinning distributed file systems &
data analysis (LO2)
2. Identifying a real data analytics problem with strong motivation for using distributed processing
methods (LO1)
3. Implementing and applying a working solution using distributed analytical techniques (LO1)
4. Critically evaluating the results of the implementation on the data with a discussion of how the
approach is different from standard non-distributed methods (e.g. relational databased, serial
data-mining) (LO2)
Grade Band E and F (E+, E, E-, F)
The candidate fails to meet the minimum requirements as outlined in the learning outcomes.
Grade Band D (D+, D, D-)
The work demonstrates significant weaknesses, but all of the learning outcomes have been met at the
minimum requirement level. The work provides evidence of some critical understanding of the concepts and
theories of large-scale data storage and processing infrastructures, and demonstrates some abilities and skills
to implement solutions using these technologies.
Grade Band C (C+, C, C-)
In addition to the requirements for a grade in D-band, the work demonstrates a critical and substantial
understanding of the concepts and theories of large-scale data storage and processing infrastructures. It
demonstrates the ability to develop an independent, systematic, logical and effective solution to the
problems identified. It also demonstrates a significant degree of competence in the appropriate use of the
relevant literature, theory, methodologies, practices, and tools, etc., to analyse the problems and evaluate
the solutions.
Grade Band B (B+, B, B-)
In addition to the requirements for a grade in C-band, the work clearly demonstrates a well-developed, critical
and substantial understanding of the concepts and theories of large-scale data storage and processing
infrastructures. It clearly demonstrates the ability to develop an independent, systematic, logical and effective
solution to the problems identified. It also demonstrates a high degree of competence in the appropriate use
of the relevant literature, theory, methodologies, practices, and tools, etc., to analyse the problems and
evaluate the solutions.
Grade Band A (A*, A+, A, A-)
In addition to the requirements for a grade in B-band, the work clearly demonstrates a sophisticated, critical
and thorough understanding of the concepts and theories of large-scale data storage and processing
infrastructures. It provides evidence of originality of thought and clearly demonstrates the ability to develop
an independent, systematic, logical and effective solution to the problems identified. It also demonstrates
excellence in the appropriate use of the relevant literature, theory, methodologies, practices, and tools, etc.,
to analyse the problems and evaluate the solutions.
FORMAT OF THE ASSESSMENT
There is no word/page limit for this assessment, but the best effort should be made to ensure the submission
is as concise as possible. You should include sections on (percentage of overall mark):
Introduction (20%) - criteria 1
Problem description & associated dataset (20%) - criteria 2
Design & Implementation (20%) - criteria 3
Results (20%) - criteria 4
Conclusions (20%) - criteria 1
Department of Computer Science
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A mark will be assigned to each section to form an overall percentage. This will then be converted into your
final grade.
SUBMISSION INSTRUCTIONS
You must submit your coursework as a PDF file on Wiseflow by 6 Jan 2020 at 11am. You can follow the link to
Wiseflow through the module’s section on Blackboard Learn or login in directly at
https://uk.wiseflow.net/brunel. The name of your file should follow the normal convention set out in the
student handbook, and must therefore include your student ID number (e.g., 0612345.pdf). It can also
include the module code (e.g., CS2001_0612345.pdf).
AVOIDING PLAGIARISM
Please ensure that you understand the meaning of plagiarism and the seriousness of the offence. Information
on plagiarism can be found on the College’s Student Handbook.
LATE COURSEWORK
The clear expectation is that you will submit your coursework by the submission deadline stated in the study
guide. In line with the University’s policy on the late submission of coursework (revised in July 2016),
coursework submitted up to 48 hours late will be accepted, but capped at a threshold pass (D- for
undergraduate or C- for postgraduate). Work submitted over 48 hours after the stated deadline will
automatically be given a fail grade (F).
Please refer to the Computer Science Student Handbook, available on Blackboard Learn, for information on
Department of Computer Science
submitting late work, penalties applied and procedures in the case of mitigating circumstances.