Oracle代写 | FIT5195 Major Assignment2020-Semester1

本次Oracle代写是根据现有数据库,设计,开发并快速从数据仓库生成报告
FIT5195 Major Assignment2020-Semester1  
select * from MonRE.<table_name>;  
The data definition of each table in MonRE is as follows:  
Table Name  
Attributes and Data Types  
Notes  
(PK/FK)  
Address  
ADDRESS_ID  
STREET  
NUMERIC  
VARCHAR property address  
This table stores the  
information. It contains  
SUBURB  
POSTCODE  
VARCHAR  
NUMBER
around 13,000 addresses in  
Australia.  
Advertisement  
Agent  
ADVERT_ID  
ADVERT_NAME  
NUMERIC  
VARCHAR information (id and name)  
of advertisements.  
This table stores the  
PERSON_ID  
SALARY  
NUMERIC  
NUMERIC  
This table stores the  
information of around  
2,000 agents (according to  
the agent ID) and the  
agent’s salary.  
Agent_Office  
Client  
PERSON_ID  
OFFICE_ID  
NUMERIC  
NUMERIC  
This table stores the  
information of which agent  
belongs to which agency  
office.  
A client in the client table  
is a person who either has  
bought/rented/inspected (i.e  
visited)/created a wishlist  
for one or more properties.  
Currently, there are  
PERSON_ID  
MIN_BUDGET  
MAX_BUDGET  
NUMERIC  
NUMERIC  
NUMERIC  
approximately over 3000  
entries in this table  
including the client’s  
person id, the maximum  
budget and minimum  
budget that the client is  
willing to spend.  
Client_Wish  
Feature  
FEATURE_CODE  
PERSON_ID  
NUMERIC  
NUMERIC  
This table stores the  
client’s wishlist details for  
some certain features that  
clients wish to have in their  
interested properties.  
This table stores  
FEATURE_CODE  
NUMERIC  
FEATURE_DESCRIPTION  
VARCHAR approximately over 700  
features of the properties  
e.g. Ensuite, Dishwasher  
etc. The id and description  
of the features are stored in  
this table.  
Office  
OFFICE_ID  
NUMERIC  
The office table stores the  
details (id and name) of the  
agency’s office. There are  
over 1000 offices.  
OFFICE_NAME  
VARCHAR  
Person  
PERSON_ID  
NUMERIC  
The person table has almost  
7
000 entries and each entry  
TITLE  
VARCHAR  
VARCHAR  
VARCHAR  
VARCHAR  
contains the details such as  
id, name, gender, contact  
details etc. about all  
FIRST_NAME  
LAST_NAME  
GENDER  
individuals associated with
ADDRESS_ID  
PHONE_NO  
EMAIL  
NUMERIC  
VARCHAR  
VARCHAR  
NUMERIC  
MonRE. A person in the  
person table could be a  
client or agent.  
Postcode  
Property  
POSTCODE  
The postcode table has over  
6
postcode information such  
as the postcode and the state  
00 entries and contains the  
STATE_CODE  
CHAR  
(e.g. 3145 VIC)  
PROPERTY_ID  
NUMERIC  
DATE  
The property table stores  
various details (such as id,  
address, type etc) about the  
properties. The property  
date added is the date in  
which the property was  
added to MonRE’s  
PROPERTY_DATE_ADDED  
ADDRESS_ID  
NUMERIC  
VARCHAR  
NUMERIC  
NUMERIC  
PROPERTY_TYPE  
PROPERTY_NO_OF_BEDROOMS  
database. There are over  
PROPERTY_NO_OF_BATHROOM  
S
PROPERTY_NO_OF_GARAGES  
6000 entries in the property  
table.  
NUMERIC  
NUMERIC  
PROPERTY_SIZE  
PROPERTY_DESCRIPTION  
VARCHAR  
NUMERIC  
Property_Advert PROPERTY_ID  
The property_advert table  
stores information about  
advertisements made by  
agents about properties.  
Each advertisement for a  
property lists the cost for  
either renting or buying the  
property.  
ADVERT_ID  
NUMERIC  
NUMERIC  
NUMERIC  
AGENT_PERSON_ID  
COST  
Property_Feature PROPERTY_ID  
NUMERIC  
NUMERIC  
NUMERIC  
NUMERIC  
NUMERIC  
NUMERIC  
DATE  
This table records which  
feature belongs to which  
property.  
FEATURE_CODE  
Rent  
RENT_ID  
This table records details of  
rents (such as property,  
client, agent, price etc.)  
AGENT_PERSON_ID  
CLIENT_PERSON_ID  
PROPERTY_ID  
RENT_START_DATE  
RENT_END_DATE  
PRICE  
DATE  
NUMERIC  
NUMERIC  
NUMERIC  
NUMERIC  
Sale  
SALE_ID  
This table records details of  
sales (such as property,  
client, agent, price etc.)  
AGENT_PERSON_ID  
CLIENT_PERSON_ID
SALE_DATE  
PROPERTY_ID  
PRICE  
DATE  
NUMERIC  
NUMERIC  
CHAR  
State  
Visit  
STATE_CODE  
The state table contains the  
code and name of the states.  
STATE_NAME  
VARCHAR  
NUMERIC  
NUMERIC  
CLIENT_PERSON_ID  
AGENT_PERSON_ID  
This table records which  
details of clients visiting  
properties during  
inspections and the agent in  
charge of the inspection.  
PROPERTY_ID  
VISIT_DATE  
DURATION  
NUMERIC  
DATE  
NUMERIC  
C. Tasks  
The assignment is divided into FOUR m ain tasks:  
1. Design a data warehouse for the above MonRE database.  
You are required to create a data warehouse for the MonRE database.  
The management is especially interested in the following fact measures:  
Average Rental Fees  
Average Agent Earning  
Average Sales  
Total Number of Rent  
Total Number of Sale  
Total Number of Agents  
Total Number of Clients  
Total Number of Properties  
Average Number of Property Visit  
The following show some possible dimension attributes that you may need in your  
data warehouse:  
Property features  
Property type (house, apartment, etc.)  
Property sales/rental time according to season  
Property location
Maximum client budget range for buying/renting properties (Low [0 to 1000],  
Medium [1001 to 100000], High [100001 to 10000000])  
Rental period (Short: < 6 months, Medium: 6-12 months, Long: > 12 months)  
Agent/Client’s gender  
Visit date (Day[Sat, Sun, … Fri], Month[Jan-Dec], Year)  
Client Wishlist  
Agent salary  
Agent office size (Small: < 4 employees, Medium: 4–12 employees, Big: >  
1
2 employees)  
Property scale (Extra Small: <= 1 bedroom, Small: 2-3 bedrooms, Medium:  
-6 bedrooms, Large: 6-10 bedrooms, Extra Large: > 10 bedrooms)  
3
Property feature category (Very basic: < 10 features, Standard: 10-20  
features, Luxurious: > 20 features)  
For each attribute, you may apply your own design decisions on specifying a range or  
a group, but make sure to specify them in your submission.  
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 are:  
a) The E/R diagram of the operational database,  
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).  
Designing the data warehouse by drawing star/snowflake schema.  
The star/snowflake schema for this data warehouse 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 identify the fact measures and dimensions:  
What is the average rental fee of apartments around South Yarra, VIC in  
2019?  
What is the average earning for all Ray White agents?  
What is the average sales for houses in VIC compared to NSW?  
Who are the top 3 agents in Melbourne?  
What is the total number of rent for clients who stay in small scale properties  
with very basic features?  
What is the total number of sales for Townhouses with Air Conditioning and  
Security?
What is the total number of female agents who work in a medium agent  
office?  
What is the total number of clients with a high budget?  
What is the total number of properties being advertised for sale in April 2020?  
What is the average number of property visits during summer?  
Which day is the most popular visit day?  
You should pay attention to the granularity of your fact tables. You are required to  
create two versionsof star/snowflake based on different levels of aggregation.  
The two versions of the star/snowflake represent different levels of aggregation.  
Version-1 should be in the highest level of aggregation. Version-2 should be in level  
0, which means no aggregation.  
Version Name  
Version-1  
Version-2  
Level  
High aggregation (e.g. Level 2)  
No aggregation (Level 0)  
The star/snowflake schema of both versions you created should contain Bridge Table  
and Temporal.You can choose to use Hierarchy or Non-Hierarchy,but you need  
to provide the reason why you use Hierarchy or Non-Hierarchy. If there is any  
Determinant Dimension, you are required to denote the Determinant Dimension  
clearly with a “*”besides your dimension’s name (e.g. “*ABC_DIM”). You can use  
different temporal data warehousing techniques for the temporal dimension and  
provide the reasons of your choice.  
The outputs of this task are:  
c) Two versions of star/snowflake schema diagrams,  
d) A short explanation of why you chose hierarchy or non-hierarchy,  
e) The reasons of the choice of SCD type for temporal dimension,  
f) A short explanation of the difference among the two versions of star/snowflake  
schema.  
2. Implement the two versions star/snowflake schema using SQL.  
You need to implement the star/snowflake schema for the two versions that you have  
drawn in Task 1 above. It means that you need to create the different fact and  
dimension tables for two versions, and populate these tables accordingly.  
When naming the fact tables and dimension tables, you need to give the identical  
name for the two versions and end with the version number to differentiate them.  
For example, “MonRE_fact_v1” for version-1 and “MonRE_fact_v2” for version-2.  
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.
If your account is full, you will need to drop all of the tables that you have previously  
created during the tutorials.  
The outputs of this task are:  
a) SQL statements (e.g. create table, insert into, etc) to create the star/snowflake  
schema Version-1  
b) SQL statements (e.g. create table, insert into, etc) to create the star/snowflake  
schema Version-2  
c) Screenshots of the tables that you have created; this includes the contents of each  
table that you have created. If the table is very big, you can only show the first  
part of the data.  
3. Create the following reports using OLAP queries.  
You are required to generate the reports using both data warehouse versions,  
version-1 (e.g. Level 2) and version-2 (Level 0 no aggregation), that you have  
implemented in Task 2. For each report, you ought to produce the SQL command and  
sample report output.  
a. Simple reports:  
Produce three reports. Each report contains two attributes from two different  
dimensions, and one fact measurement.  
For the report itself, the first report must be about Top k, the second report is Top  
n%, and the third report is Show All.​  
The outputs of this task are:  
(
(
a) The query questions written in English,  
b) Your explanation on why such a query is necessary or useful for the  
management,  
(
(
c) The SQL commands, and  
d) The screenshots of the query results (or part of the query results), including all  
attribute names.  
b. Reports with proper sub-totals:  
Produce four reports. These reports must include sub-totals, using the Cube or  
Roll-up or Partial Cube/Roll-up operators.  
REPORT 4 and REPORT 5: What are the sub-total and total rental fees from  
each suburb, time period, and property type? (You must use the Cube and Partial  
Cube operator)
REPORT 6 and REPORT 7: Produce 2 other sub-totals reports that are useful for  
management using Roll-up and Partial Roll-up.  
The outputs of this task are:  
(
(
a) The query questions written in English,  
b) Your explanation on why such a query is necessary or useful for the  
management,  
(
(
c) The SQL commands that include sub-totals, using the Cube or Roll-up or  
Partial Cube/Roll-up operators, and  
d) The screenshots of the query results (or part of the query results).  
c. Reports with moving and cumulative aggregates:  
Produce three reports containing moving and cumulative aggregates.  
REPORT 8: What is the total number of clients and cumulative number of clients  
with a high budget in each year?  
REPORT 9 and REPORT 10: Produce 2 other moving/cumulative aggregate  
reports that are useful for management.  
The outputs of this task are:  
(
(
a) The query questions written in English,  
b) Your explanation on why such a query is necessary or useful for the  
management,  
(
(
c) The SQL commands that contains moving and cumulative aggregates, and  
d) The screenshots of the query results (or part of the query results).  
d. Reports with Partitions:  
Produce two reports that contain partitions.  
REPORT 11: Show ranking of each property type based on the yearly total  
number of sales and the ranking of each state based on the yearly total number of  
sales.  
REPORT 12: Produce another partitioning report that is useful for management.  
The outputs of this task are:  
(
(
a) The query questions written in English,  
b) Your explanation on why such a query is necessary or useful for the  
management,  
(
(
c) The SQL commands that contains partitions, and  
d) The screenshots of the query results (or part of the query results).  
4. Business Intelligence (BI) Reports.  
Choose any five reports from Task 3, and change the presentation of these reports by  
representing these in a graph format. This new presentation should be more appealing  
to the management. You can use any graph software to show the graph reports.  
Additionally, in these new reports, you might want to include some selection buttons  
(
for illustrative purposes), which may give users options on what criteria to choose, so  
that the graph report will be more dynamic.

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