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Assessment regulations

23 Oct
Dr. P.I. Chountas 6BUIS001W
University of Westminster
Department of Computer Science

2017/18

6BUIS001W Business Intelligence – Coursework 1 (2017/18)
Module leader Dr. P.I. Chountas
Unit Coursework 1
Weighting: 50%
Qualifying mark 30%
Description Show evidence of understanding of various Business Intelligence
concepts, through the design of data marts and implementation of OLAP
queries. Implementation is performed in R/MySQL environment, while
students need to perform OLAP queries and investigate the use of R for
performing multidimensional analysis over Big Data Sets.
Learning Outcomes
Covered in this
Assignment:
This assignment contributes towards the following Learning Outcomes
(LOs):
L01 Analyse data resource architectures, management process for
information resource integration and the process of establishing
data models in order to build data warehouse that assists
management in decision making process.
L02 Reflect data discovery strategies in order to recommend an
appropriate method for incorporating business intelligence in
industrial environments.
L05 Implement a dynamic and interactive decision support system
That applies the concept of knowledge discovery &
information retrieval on a large scale business information
resource.
Handed Out: 05TH OCTOBER 2017
Due Date 07TH NOVEMBER 2017
Submission by 10:00am
Expected deliverables Submit on Blackboard a zip file containing the required documentation
(either in docx or pdf format). All implemented codes should be included
in your documentation together with the results/analysis.
Method of Submission: Electronic submission on BB via a provided link close to the submission
time.
Type of Feedback and
Due Date:
Feedback will be provided on BB, on 28th November 2017 (15 working
days)
BCS CRITERIA MEETING IN
THIS ASSIGNMENT
‘Knowledge and understanding of facts, concepts, principles &
theories’
‘Knowledge and understanding of mathematical and/or statistical
principles’

Dr. P.I. Chountas 6BUIS001W 2017/18
Assessment regulations
Refer to section 4 of the “How you study” guide for undergraduate students for a clarification of how you are
assessed, penalties and late submissions, what constitutes plagiarism etc.
Penalty for Late Submission
If you submit your coursework late but within 24 hours or one working day of the specified deadline, 10 marks
will be deducted from the final mark, as a penalty for late submission, except for work which obtains a mark in
the range 40 – 49%, in which case the mark will be capped at the pass mark (40%). If you submit your
coursework more than 24 hours or more than one working day after the specified deadline you will be given a
mark of zero for the work in question unless a claim of Mitigating Circumstances has been submitted and
accepted as valid.
It is recognised that on occasion, illness or a personal crisis can mean that you fail to submit a piece of work on
time. In such cases you must inform the Campus Office in writing on a mitigating circumstances form, giving
the reason for your late or non-submission. You must provide relevant documentary evidence with the form.
This information will be reported to the relevant Assessment Board that will decide whether the mark of zero
shall stand. For more detailed information regarding University Assessment Regulations, please refer to the
following website:
http://www.westminster.ac.uk/study/current-students/resources/academic-regulations
Dr. P.I. Chountas 6BUIS001W 2017/18
Coursework Description
The following relational logical schema describes an operational database for a car dealer :
Primary key are underlined. Foreign key are denoted as follows
<attribute name>, followed
by <: parent_table_name>
Dealer (OfficeName, City, Area, State, Country)
CARS (LicensePlate, Category, Model, Brand, Fuel, RegistrationDate)
HAVE_OPTIONAL (LicensePlate: CARS, Optional)
Rentals (LicensePlate: CARS, PickupDate, DropoffDate, PickupPlace: DEALER,
DropoffPlace: DEALER, Mileage)
SALES (LicensePlate: CARS, PickupDate, Location :DEALER)
DRIVERS (LicenseNumber, LicenseExpiration, DriverName, Birthdate)
DRIVE (LicenseNumber: DRIVERS,(LicensePlate, PickupDate):RENTALS)
INSURANCES (Risk,(LicensePlate, PickupDate):RENTALS, Cost)
PAYMENTS ((LicensePlate, PickupDate): SALES:RENTALS, Amount, Discount,
PaymentMode)
Some hidden functional dependencies hold: City->State->Country->Area, & Model->Brand.
Tasks
1. Inspect the source schema above, then identify the main Business Processes- facts of
interest and design a single fact schema.
a) Identify the main Business Processes and their logical schema including
primary and foreign keys. Justify your answer;
[3 Marks]
b) For each Business Processes identify the involved dimensions and their logical
schema, including primary and foreign keys;
[3 Marks]
c) For each Business Process identify the main measures. Justify your answer;
[3 Marks]
d) Identify any conforming dimensions and their treatment as part of a single
fact schema;
[3 Marks]
e) Design a single fact schema that represents all the identified business
processes, conforming dimensions and measures. Note: for the graphical
representation need of the above task you may employ UML class Diagrams;
[3 Marks]
f) Implement the single fact schema as a data mart solution under MySQL and
populate it with sample data using the R/MySQL environment.
[5 Marks]
Dr. P.I. Chountas 6BUIS001W 2017/18
2. Provide the OLAP queries to answer the following questions:
a. Which car models are rented frequently?
[4 Marks]
b. Which car models are sold locally?
[5 Marks]
c. What is the percentage contribution of rentals and sales in terms of payments?
[6 Marks]
Marks for 2a-2c, will be awarded as follows: 60% for correct query formulation and 40%
for appropriate visualisation of results.
3. Investigate the suitability of R to perform BI queries on Big Data repositories :
Analysing big data could be very difficult using classical means like relational database
management systems or desktop software packages for multidimensional analysis and
visualization. Instead, big data requires large clusters with hundreds or even thousands of
computing nodes. Big data sets are stored on clusters of commodity hardware such as
Hadoop.
Task
a) You are required to investigate the possibilities of integrating Big Data
Technologies with R. Present at least three ways of integrating them and
emphasise the advantages and disadvantages of each solution;
Present your findings for the above questions in the form of a research paper.
The paper must express your own conclusions and findings. The paper size
should be between [600-700] words, excluding references.
Papers violating the lower limit or exceeding the upper limit of allowable words
will be subject to a penalty of 10%, (1.5 Marks out of 15).
[15 Marks]
Dr. P.I. Chountas 6BUIS001W 2017/18
Coursework Marking scheme
Due to the nature of the assessment candidates may come up with more than one equally, correct solutions.
Thus marks will be allocated as follows
1. Inspect the source schema, then identify the main Business Processes- facts of
interest and design a single fact schema.
a) Identify the main Business Processes and their logical schema including
primary and foreign keys. Justify your answer;
[3 Marks]
Business Processes/ Facts (Sales, Rentals, Insurances, Payment is not a separate fact)
[1.5 Marks]

Proper Justification [1.5 Marks]
b) For each Business Processes identify the involved dimensions and their logical
schema, including primary and foreign keys;

[3 Marks]
Dimensions (Dealer, Time, Driver, Cars) [1.5 Marks]
Proper Justification [1.5 Marks]
c) For each Business Process identify the main measures. Justify your answer;
[3 Marks]
Measure (Amount, Discount, Mileage) [1.5 Marks]
Proper Justification [1.5 Marks]
d) Identify any conforming dimensions and their treatment as part of a single
fact schema;
[3 Marks]
Conforming Dimensions (Dealer, Time, Cars) [1.5 Marks]
Treatment of conforming Dimensions [1.5 Marks]
e) Design a single fact schema that represents all the identified business
processes, conforming dimensions and measures. Note: for the graphical
representation need of the above task you may employ UML class Diagrams;
[3 Marks]
Use of UML class diagram or other containing Sales, Insurances and Rentals as the two
identified facts, and the involved dimensions (time, dealer, cars, driver). Include (Amount,
Discount,Cost and Mileage) as the measures for Sales, Insurances and Rentals.
f) Implement the single fact schema as a data mart solution under MySQL and
populate it with sample data using the R/MySQL environment.
[5 Marks]
Implantation of facts and measures [1.5 Marks]
Treatment of conforming dimensions [1.5 Marks]
Implementation of entity integrity and referential constraints [2 Marks]
Dr. P.I. Chountas 6BUIS001W 2017/18
2. Provide the OLAP queries to answer the following questions:
a. Which car models are rented frequently?
[4 Marks]
b. Which car models are sold locally?
[5 Marks]
c. What is the percentage contribution of rentals and sales in terms of payments?
[6 Marks]
Marks for 2a-2c, will be awarded as follows: 60% for correct query
formulation and 40% for appropriate visualisation of results.
3. Investigate the suitability of R to perform BI queries on Big Data repositories :
Analysing big data could be very difficult using classical means like relational
database management systems or desktop software packages for multidimensional
analysis and visualization. Instead, big data requires large clusters with hundreds or
even thousands of computing nodes. Big data sets are stored on clusters of
commodity hardware such as Hadoop.
Task
a) You are required to investigate the possibilities of integrating Big Data Technologies
with R Present at least three ways of integrating them and emphasise the
advantages and disadvantages of each solution;
Present your findings for the above questions in the form of a research paper.
The paper must express your own conclusions and findings. The paper size
should be between [600-700] words, excluding references.
Papers violating the lower limit or exceeding the upper limit of allowable words
will be subject to a penalty of 10%, (1.5 Marks out of 15).
[15 Marks]

Marks will be allocated for
o Originality of the report
[3 Marks]
o Critical analysis [3 Marks]
o Technical content [4 Marks]
o Clarity of the paper [3 Marks]
o Use of references [2 Marks]

 

 
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Posted by on October 23, 2017 in academic writing, Academic Writing

 

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