Objective:
| · The objective of this assignment is to understand BI framework, creating star/
snowflake schema, and concept of sentiment and semantic analysis. |
Assignment Rubric
| Excellent
(25%) |
Proficient (15%) | Marginal (5%) | Unacceptable
(0%) |
Problem #
where applied |
|
| Completeness | All required | Submission | Some tasks are | Incorrect and | Problem # 3 |
| including
Citation |
tasks are
completed |
highlights tasks
completion. However, missed some tasks in between, which created a disconnection |
completed,
which are disjoint in nature. |
irrelevant | |
| Correctness | All parts of the | Most of the given | Most of the | Incorrect and | Problem #2 |
| given tasks are
correct |
tasks are correct
However, some portions need |
given tasks are
incorrect. The submission |
unacceptable |
| minor
modifications |
requires major
modifications. |
||||
| Novelty | The submission | The submission | The submission | There is no | Problem #1 |
| contains novel
contribution in key segments, which is a clear indication of application knowledge |
lacks novel
contributions. There are some evidences of novelty, however, it is not significant |
does not contain
novel contributions. However, there is an evidence of some effort |
novelty | ||
| Clarity | The written or | The written or | The written or | Failed to prove | Problem #1 |
| graphical | graphical | graphical | the clarity. Need | ||
| materials, and | materials and | materials, and | proper | ||
| developed | developed | developed | background | ||
| applications | applications do | applications fail | knowledge to | ||
| provide a clear
picture of the concept, and highlights the clarity |
not show clear
picture of the concept. There is room for improvement |
to prove the
clarity. Background knowledge is needed |
perform the tasks |
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/hourly-
weat he r-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 1/2 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.
- 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.
- In addition to justification, attach screenshot of the model (star schema or snowflake schema) in the PDF file.
- 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,
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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 (C c 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. news 1 = “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
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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.
| News Article | News
Description/content |
match | polarity |
| 1 | Canada is cold cold. I
feel good not bad |
cold, good, not, bad | negative |
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)
- 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= 5 0
Now, use the search query “Canada”, “Moncton”, “Toronto”, and search in how many documents these words have appeared.
| Total Documents | 5 0 | ||
| 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 | 5 0/ 5 | 1 |
| Toronto | 10 | 50/10 | 0.698970004 |
- 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 #3 0 | 8 | 1 |
- 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:
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- 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.
- 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).
- Your executable code/script needs to be submitted on https://git.cs.dal.ca/
