Description
Objective:
• The objective of this assignment is to understand BI framework, creating star/
snowflake schema, and concept of sentiment and semantic analysis.
Plagiarism Policy:
• This assignment is an individual task. Collaboration of any type amounts to a
violation of the academic integrity policy and will be reported to the AIO.
• Content cannot be copied verbatim from any source(s). Please understand the
concept and write in your own words. In addition, cite the actual source. Failing to
do so will be considered as plagiarism and/or cheating.
• The Dalhousie Academic Integrity policy applies to all material submitted as part of
this course. Please understand the policy, which is available at:
https://www.dal.ca/dept/university_secretariat/academic-integrity.html
Assignment Rubric
Excellent
(25%)
Proficient (15%) Marginal (5%) Unacceptable
(0%)
Problem #
where
applied
Completeness
including
Citation
All required
tasks are
completed
Submission
highlights tasks
completion.
However, missed
some tasks in
between, which
created a
disconnection
Some tasks are
completed,
which are
disjoint in
nature.
Incorrect and
irrelevant
Problem #3
Correctness All parts of the
given tasks are
correct
Most of the given
tasks are correct
However, some
portions need
Most of the
given tasks are
incorrect. The
submission
Incorrect and
unacceptable
Problem #2
Summer 2021 saurabh.dey@dal.ca
minor
modifications
requires major
modifications.
Novelty The submission
contains novel
contribution in
key segments,
which is a clear
indication of
application
knowledge
The submission
lacks novel
contributions.
There are some
evidences of
novelty,
however, it is not
significant
The submission
does not contain
novel
contributions.
However, there
is an evidence of
some effort
There is no
novelty
Problem #1
Clarity The written or
graphical
materials, and
developed
applications
provide a clear
picture of the
concept, and
highlights the
clarity
The written or
graphical
materials and
developed
applications do
not show clear
picture of the
concept. There is
room for
improvement
The written or
graphical
materials, and
developed
applications fail
to prove the
clarity.
Background
knowledge is
needed
Failed to prove
the clarity. Need
proper
background
knowledge to
perform the tasks
Problem #1
Citation:
McKinney, B. (2018). The impact of program-wide discussion board grading rubrics on students’ and faculty
satisfaction. Online Learning, 22(2), 289-299.
Tasks
• This assignment requires you to submit programming codes on gitLab, and a single PDF
file on Brightspace.
Problem #1
Business Intelligence Reporting using Cognos
1. Download the weather dataset available on https://www.kaggle.com/PROPPG-PPG/hourlyweather-surface-brazil-southeast-region?select=sudeste.csv
2. Explore the dataset and identify data field(s) that could be measured by certain factors or
dimensions. (Follow recorded lecture #18, and synchronous session #18)
Example: In a Sales dataset, you may find a measurable field “total sales”, which could be
analyzed by other factors such as, “products”, “time”, “location” etc. These factors are known as
dimensions. Depending on the data, you may also find possibilities of slice and dice, i.e. analysis
could be possible in more granular level; From total sales by city to total sales by store
3. Write ½ page explanation on how did you select the measurable filed, i.e. fact and what are the
possible dimensions. Include this part in your PDF file.
Summer 2021 saurabh.dey@dal.ca
4. Clean the dataset, if required perform formatting. You can perform the cleaning and formatting
using spreadsheet operation or programming script. If you use program add that in GitLab, if
you use other methods, write the steps in the PDF file.
5. Create Cognos account and import your dataset. Identify the dimensions, and create/import the
dimension tables.
6. Based on your understanding of the domain (please read the information/metadata available
on the dataset source, i.e. Kaggle), create star schema or snowflake schema. Provide justification
of your model creation in the PDF file.
7. In addition to justification, attach screenshot of the model (star schema or snowflake schema)
in the PDF file.
8. Display visual analysis of the data in a suitable format, e.g. bar graph showing temperature
change in terms of a suitable dimension. Add the screenshot of the analysis on the pdf or add a
screen recording of the analysis on your .zip folder.
Problem #2
Sentiment Analysis – Java Program only
1. To perform this task, you need to consider the processed news (“content or descriptions” only,
ignore other fields) that you obtained and stored in MongoDB in your previous assignment. If you
could not perform/complete the task, then obtain the processed MongoDb News collection by
contacting your TA Kethan (Cc me in that email)
2. Write a script to create bag-of-words for each news article. (code from online or other sources
are not accepted)
e.g. news1 = “Canada is cold cold. I feel good not bad”
bow1 = {“Canada”:1, “is”:1, “cold”:2, “I”:1, “feel”:1, “good”:1, “not”:1, “bad”:1}
You do not need any libraries. Just implement a simple counter using loop.
Compare each bag-of-words with a list of positive and negative words. You can download list of
positive and negative words from online source(s). You do not need any libraries. Just perform
word by word comparison with a list of positive and negative words that you can get from any
online platform. E.g. negative words can be found here https://gist.github.com/mkulakowski2/4289441
3. Tag each news as “positive”, “negative”, or “neutral” based on overall score. You can add an
additional column to present your finding.
E.g. frequencies of the matches “cold”=2, “not” =1, “bad”=1 (negative -4), “good”=1 (positive +1). Overall score = -3
News Article News
Description/content
match polarity
1 Canada is cold cold. I
feel good not bad
cold, good, not, bad negative
Summer 2021 saurabh.dey@dal.ca
Problem #3
Semantic Analysis
1. For this task, consider the processed news collection that you created in Assignment 3.
2. Use the following steps to compute TF-IDF (term frequency-inverse document frequency)
a. Suppose, you have 50 news articles (description or content only) that are stored in
50 JSON arrays. You need to consider these data points as the total number of
documents (N). In this case N=50
Now, use the search query “Canada”, “Moncton”, “Toronto”, and search in how many
documents these words have appeared.
Total Documents 50
Search Query Document
containing
term(df)
Total Documents(N)/ number
of documents term appeared
(df)
Log10(N/df)
Canada 30 50/30 0.221848749
Moncton 5 50/5 1
Toronto 10 50/10 0.698970004
b. Once you build the above table, you need to find which document has the highest
occurrence of the word “Canada”. You can find this by performing frequency count of
the word per document.
Term Canada
Canada appeared in 30 documents Total Words (m) Frequency (f )
Article #1 6 2
Article #2 10 1
: : :
Article #30 8 1
c. You should print the news article (programmatically), which has the highest relative
frequency. You can find this by computing (f/m).
Assignment 4 Submission Format:
1) Compress all your reports/files into a single .zip file and give it a meaningful name.
You are free to choose any meaningful file name, preferably – BannerId_Lastname_firstname_5408_A4
but avoid generic names like assignment-4.
2) Submit your reports only in PDF format.
Please avoid submitting .doc/.docx and submit only the PDF version. You can merge all the reports into
a single PDF or keep them separate. You should also include output (if any) and test cases (if any) in the
PDF file in the proper format (e.g. tables, charts etc. as required).
3) Your executable code/script needs to be submitted on https://git.cs.dal.ca/
