INFS3200 Sem 2, 2024
Assignment 2: Data Warehouse
Guidelines for Assignment Submission:
1. Submission Deadline: October 18
th
, 2024, 15:00 (AEST).
2. For questions requiring queries, kindly include both a screenshot of the query and a
screenshot of the execution results.
3. When addressing discussion-based questions, provide comprehensive textual
responses, supplemented with suitable visual aids if necessary.
4. The submission should be in PDF format, named as ‘A2_s1234567.pdf’ (replace
‘1234567’ with your student ID). The submitted file should not exceed 10 MB.
5. All implementations and project components should be finalized within the
UQZones environment. Evaluators might assess the assignment based on the
established checkpoints within UQZones.
6. All assignment submissions must be submitted exclusively through the UQ
Blackboard. Alternative methods of submission will not be acknowledged. Please be
mindful that submissions via email will not be entertained under any circumstances.
7. It is imperative to adhere to the stipulated submission deadline to avoid penalties, as
explained in the Educational Course Policies (ECP).
8. Ensuring the successful submission of your assignment within the designated
timeline is your responsibility.
Task 1: Data Linkage (3 points)
In this part, you should use the restaurant dataset provided in Prac3. Import data into
PostgreSQL database or directly read the data, and complete the following tasks:
1. (1 point) Link two two restaurant records by using edit-distance as the similarity
measure. Report the hyper-parameter choice and the total number of similar
records.
2. (1 point) Link two two restaurant records by using tri-grams as the similarity
measure. Report the hyper-parameter choice and total number of similar records.
3. (1 point) Which similarity measure is better for the restaurant dataset? Provide the
justifications.
Task 2: Data Warehouse (7 points)
You are provided with an electronic device wholesale dataset in CSV format. Download the
dataset by using your UQZone terminal:
curl -O https://stluc.manta.uqcloud.net/infs3200/public/Sales.csv
The dataset is exported from an OLTP system. Each record (row) in the dataset indicates a
sales transaction. You are required to construct a data warehouse to analyze the sales
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