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数据库代写 | COMP 7810/4096 Business Intelligence

这个作业是根据之前创建的数据库完成一些查询等操作
COMP 7810/4096 Business Intelligence
Individual Project (2019-20)
Consider a database of NBA and ABA statistics derived from [1]. The database contains 6 relations stored in excel
formats:
1. Players – List of all players and their information
2. Regular Season – Regular season statistics for all players
3. Playoffs – Playoff statistics for all players
4. Teams – List of all teams and their information
5. Team Season – Regular season team offense and defense statistics, won and lost statistics
6. State – Teams’ state code and state name
These 6 relations are not structured for efficient online analytical processing (OLAP). However, analysts of NBA
and ABA prefer to analyze the teams’ and players’ statistics of NBA and ABA by using Microsoft SQL Server and
SSDT.
Produce a report (in docx format) which contains the answers of tasks 1-4:
 Task 1: Import the Excel files to the database using SSMS. Combine Teams and State tables to form one
new table [Teams_State] using a Transformation function in SSIS. Capture the screenshots for the
transformation flow and describe the details for the ETL process. Perform normalization or
denormalization if necessary. Design a schema of a data mart. Perform proper data selection and stability
analysis. Briefly explain your schema design. Specify your assumptions.
 Task 2: Implement the data warehouse using SSDT, you should contain evidences of the implementation
(such as screenshots of data source view, cube, deployment messages, and details in Solution Explorer).
Add dimension intelligence if necessary. In addition, indicate the following clearly:
1. Fact table(s)
2. Dimension table(s)
3. Primary key(s)
4. Foreign key(s)
5. Attributes and user hierarchies (if any) in each dimension
 Task 3: Create two calculated fields in your schema. The offensive_index is the first calculated field
defined by the sum of all attributes of offense (i.e., the attributes with name o_xxx). Similarly, create the
second calculated field called defense_index (i.e., sum of the attributes with name d_xxx)
Please submit your project to BUelearning website
30 April 2020 (Thu) 6 pm
offense_index = [sum of all attributes of offense (i.e., the attributes with name o_xxx)]
defense_index = [sum of all attributes of defense (i.e., the attributes with name d_xxx)]
 Task 4: Use MDX to get the results of the following questions. Copy the MDX and the screenshots of the
results; paste them in your report. You need to remove NULL values for all questions (if any) and create a
chart to show the results using SQL server report service (SSRS) for each question.
1. The offensive index and defensive index for the teams Chicago Stags, Floridians and
Orlando Magic.
2. The won and lost index for each teams (with team code and team name) in the state of Florida.
3. The top 10 teams (with team code and team name) having the best offensive index for all years.
4. The teams (showing team name) with defensive index greater than 1300000, sort the index
in descending order.
5. The top 5 players (showing IDs and names) with the best Pts in Playoffs and weight between
135 and 150 (using where clause).
6. The won and lost indexes for BOS team in 1950 and NYK team in 1951.
7. The bottom 5 teams (showing team name) with the worst defensive index for all years.
8. The won and lost index for each team (showing team name) during 1950 to 1952.
9. Use ORDER () function with order specification BDESC to sort the lost index from highest to lowest
for different teams during 1949 to 1950. (Hint: You should NOT sort the performance within each
year but for all 2 years)
10. The team (showing team code) with the best offensive index in each year.
11. The turnover index in Playoffs (with their IDs and names) where player’s position is G and
turnover index greater than 300
12. The top 5 players who played the longest minutes in Regular Season with position F.
13. Average minutes for different positions in Playoffs. (Format the average value to 1 d.p.) [Hint: you
may need to use the measure Playoffs Count]
14. Compare the offensive index for each team (showing the team code) in 1951 and 1952 using
prevMember or nextMember, and calculate the differences.
Deliverables
Please submit
1) your codes i.e. visual studio project files (in zip format), and
2) your report (in pdf format) [please covert the docx to pdf]
to the site http://buelearning.hkbu.edu.hk/
It is preferred to be concise, which should avoid excessive printouts of result data.
References:
[1] A Moore. Datasets and project suggestions. Available at
http://www.cs.cmu.edu/~awm/10701/project/data.html. Last accessed Mar 2013.
[2] http://stackoverflow.com/questions/8321014/performance-tips-for-top-5-bs-for-each-a-mdx-queriesespecially-ssas
[3] http://bimentalist.com/2011/08/29/msbi-28-ssas-9-modifying-measures-attributes-and-hierarchies-inssas/
Notes:
Penalty will be imposed on the act of academic dishonesty such as plagiarism, or submission of materials for
assessment which is not your own work. A student found to have committed an act of plagiarism shall receive an
“F” grade for the course. You are strongly advised to read the handbook on Avoiding Plagiarism, especially the
section on “The Cost of Plagiarism”. The handbook is available on the Academic Registry website at
http://buar.hkbu.edu.hk/index.php/current_students_and_alumni/academic_guidelines/avoiding_plagiarism
Hints:
You may need to look up MDX reference books or online manual to look for “build-in” functions to complete your
tasks, which is a common practice in real world.
Table 1 Meaning of Attributes (Non-exhaustive) of the Dataset
Attribute name Meaning
fgm Field Goals Made
fga Field Goals Attempted
ftm Free Throws Made
fta Free Throws Attempted
3pm Three Points Made (Team)
3pa Three Points Attempted (Team)
tpm Three Points Made (Player)
tpa Three Points Attempted (Player)
oreb Offensive Rebound
dreb Defensive Rebound
reb Total Rebound (oreb + dreb)
asts Assists
pf Personal Fouls
stl Steals
to Turnovers
blk Blocks
pts Points
gp Game Played
o_* or d_* Offensive Stats or Defensive Stats for Team


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