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FIT5137 Assignment 2 -S2 2024  (Weight = 40%)

Due - Friday, 20 September 2024, 4:30 PM

General Information and Submission

o This is an individual assignment.

o Submission method: Submission is online through Moodle.

o Penalty for late submission: 5% deduction for each day.

o Assignment FAQ: There is an Assignment Frequently Asked Questions page set up for the Assignment 2 on EdStem Forum.

Problem Description

M-Stay is a residential service that offers homestay and rental services to Monash students and staff around Melbourne. The company has an existing operational database that maintains and stores all of the business transactions information (e.g. properties, hosts, listings, booking, etc.) required for the management's daily operation. As the business grows, M-Stay has decided to build a Data Warehouse to improve their analysis and work efficiency. However, since the staff at M-Stay have limited Business Intelligence and Data Warehouse knowledge, they have decided to hire you to design, develop and quickly generate BI reports from a Data Warehouse.

The operational database tables can be found at the MStay account. You can, for example, execute the following query:

select * from MStay.<table_name>;

The data definition of each table in MStay is as follows:

Table Name	Attributes,Data Types and Key Constraints		Notes
REVIEW	Review_ID	Number (PK)	The table stores review information of the related booking order.
	Review_Date	Date
	Review_Comment	Varchar2
	Booking_ID	Number (FK)
BOOKING	Booking_ID	Number (PK)	The table stores booking information.
	Booking_Date	Date
	Booking_Stay_Start_Date	Date
	Booking_Duration	Number
	Booking_Cost	Number
	Booking_Num_Guests	Number
	Listing_ID	Number (FK)
	Guest_ID	Number (FK)
GUEST	Guest_ID	Number (PK)	The table stores all guest information.
	Guest_Name	Varchar2
LISTING	Listing_ID	Number (PK)	The table stores all listing information. Each listing has one property and one host information.
	Listing_Date	Date
	Listing_Title	Varchar2
	Listing_Price	Number
	Listing_Min_Nights	Number
	Listing_Max_Nights	Number
	Prop_ID	Number (FK)
	Type_ID	Number (FK)
	Host_ID	Number (FK)
HOST	Host_ID	Number (PK)	The table stores all host information.
	Host_Name	Varchar2
	Host_Since	Date
	Host_Location	Varchar2
	Host_About	Varchar2
	Host_Listing_Count	Number
HOST_VERIFICATION	Host_ID	Number (PF)	The table stores the verification information between host and channel.
	Channel_ID	Number (PF)
CHANNEL	Channel_ID	Number (PK)	The table stores the channel of verification for the hosts.
	Channel_Name	Varchar2
LISTING_TYPE	Type_ID	Number (PK)	The table stores all listing types.
	Type_Description	Varchar2

A. Transformation Stage

The first stage of this assignment is divided into TWO main tasks:

1. Design a data warehouse for the above M-Stay database.

You are required to create a data warehouse for the M-Stay database.

The management is especially interested in the following indicators :

● Number of reviews

● Number of listings

● Average booking cost

The following is a list of dimension attributes that you should include in your data warehouse:

● Listing type

● Listing time [Month, Year]

● Listing season

o (Spring:9 to 11, Summer: 12 to 2, Autumn: 3 to 5 and Winter: 6 to 8)

● Listing maximum stay duration [short-term: less than 14 nights, medium-term: 14 to 30 nights, long-term: more than 30 nights]

● Listing price range [low: less than $100, medium: $100 to $200, high: more than $200]

● Channels

● Booking duration [short-term: less than 30 nights, medium-term: 30 to 90 nights, long-term: more than 90 nights]

● Review time [Month, Year]

● Booking cost range [low: less than $5000, medium: $5000 to $10000, high: more than $10000]

For the attribute, ensure that it meets the requirements of the range or group specified in your submission, if required in the specification.

- Preparation stage.

Before you start designing the data warehouse, you have to ensure that you have explored the operational database and have done sufficient data cleaning. Once you have done the data cleaning process, you are required to explain what strategies you have taken to explore and clean the data.

The outputs of this task for Report are:

a) If you have done the data cleaning process, explain the strategies you used in this process (you need to show the SQL to explore the operational database and SQL of the data cleaning, as well as the screenshot of data before and after data cleaning).

- Designing the data warehouse by drawing star/snowflake schema.

Design task A:

The star schema for this data warehouse may contains multi-facts. You need to identify the fact measures, dimensions, and attributes of the star/snowflake schema. The following queries might help you to determine the fact measures and dimensions:

● How many long-term stay duration listings are listed on Facebook?

● How many listings are listed in June 2015?

● How many listings are there in summer for an “Entire home/apt” in a medium price range?

● How much is the average booking cost in March 2013?

● How many bookings were there for “Private rooms” with a short-term stay duration in 2015?

● How many high-cost bookings were made in April 2014?

● How many reviews were given in February 2016?

Note: the star schema you created in Design Task A as the highest level of aggregation

Design task B:

In this assignment, consider the star schema you created in Design Task A as the highest level of aggregation. The M-Stay company manager wants to implement a drill-down function to explore more detailed information. Your task is to suggest several ways to increase the granularity of your fact tables from Design Task A. In other words, the manager wants to decrease the aggregation level of the fact tables you created in Design Task A.

The outputs of task A & B for Report are:

b) A  star/snowflake schema diagrams for design task A. (You can use Lucidchart to draw the star schema.)

c) List suggestion of  increase the granularity of your fact tables for design task B

2. Implement design task A star/snowflake schema using SQL.

You are required to implement the star/snowflake schema that you have drawn in design task A. This implies that you need to create the fact and dimension tables in SQL. The output is a series of SQL statements to perform this task. You will also need to show that this task has been carried out successfully.

Note:

● If your account is full, you will need to drop all of the tables that you have previously created during the tutorials.

● If you have dropped all tables in your account and you still encounter the ORA-01536: space quota exceeded for tablesace ‘TABLE_NAME’, please check your SQL code whether you have properly joined all tables. This issue was mainly caused when you did not do the table join properly as the number of records multiplied during the process.

The outputs of this task for Report are:

a) Screenshots of the table structure you created for Design Task A, including the dimension tables and fact tables.

A sample of screenshots of the table structure

B. Data Analytic Stage

Conduct a data analysis using the star schema you created in Design Task A by writing SQL queries to explore the data further. Present your findings in a clear and concise manner, demonstrating your understanding of the dataset and highlighting any noteworthy observations or patterns.

The outputs of this task for Report are:

1. Findings report: A detailed explanation of your findings, including any significant observations or patterns identified during the analysis.

Submission Checklist

Step 1: Report (25% of the total score)

A combined pdf file save as: YourstudentID_A2_report.pdf, containing all of the above tasks:

A. Cover page

B. If you have done the data cleaning process, explain the strategies you used in this process (you need to show the SQL to explore the operational database and SQL of the data cleaning, as well as the screenshot of data before and after data cleaning). Note that you are only required to find around 5 (five) data errors for this stage.

C. A  star/snowflake schema diagrams for design task A

D. List suggestion of  increase the granularity of your fact tables for design task B

E. Screenshots of the table structure you created for Design Task A only, including the dimension table and fact tables.

a. SQL file for creating the star/snowflake schema is NOT required in submission

F. Findings report: A detailed explanation of your findings, including any significant observations or patterns identified during the analysis.

Step 2: Poster (35% of the total score)

One page standard A4 poster in PDF format to save as: YourstudentID_A2_poster.pdf

Extract key information from the report you created and present it in a one-page poster. The poster must be in standard A4 size and in PDF format, which can be either landscape or portrait. The content should be clear and easy to understand. Avoid using technical jargon or complex language. Review the poster before submission to ensure it effectively communicates the key messages of your report.

Note:

Ensure the poster content is consistent with the key structure and findings of your report, and choose an appropriate layout that effectively organizes the information in a clear and logical manner. Maintain a good balance of text and visuals to enhance readability, and ensure all visuals are relevant and support the content of the poster. Label all visuals clearly and provide captions where necessary. Avoid overcrowding the poster with too much text or too many visuals, and ensure the poster is free of any grammatical or typographical errors.

Key guidance of design a poster:

● What is the main theme/objective of the poster that you want to express?

● Who is your target audience for this poster?

● Do you really need all the details from your report on this poster?

Step 3: Video presentation (40% of the total score)

A five minute video presentation in mp4 format save as: YourstudentID_A2_video.mp4

Based on the report and poster you have created, present your design and findings in a five-minute video presentation. Ensure you thoroughly understand both the report and the poster to effectively extract and communicate the key points.