Buena Vista Elementary School

Buena Vista Elementary School

Avani Jariwala | April Song | Ben Yook | Elaine Huang Elim Kuk | Felicia Angesti | Hyun-Ho Jung

Kyle Ong | Mitou Nguyen

Buena Vista Elementary School at a glance:

◊ Located in Walnut Creek, CA◊ Features 534 students and 54 total staff

members◊ Currently using web-based database program

called AERIES

IntroIntro EER Schema Queries Normalization

Intro

Current Database Problems:

◊ Too complicated and difficult to use◊ Web-based◊ Regular subscription cost

Our Database Solutions:

◊ User-friendly interface◊ Offline functionality◊ One-time cost for Microsoft Access

IntroIntro EER Schema Queries Normalization

Intro

EEREERIntro Schema Queries Normalization

EER

SchemSchemaa

EERIntro Queries Normalization

Schema

SchemSchemaa


Schema

SchemSchemaa


Schema

Query 1

What is the correlation between tutoring hours and academic

performance?

QueriesQueriesEER SchemaIntro Normalization

Query 1

Purpose

Finding whether or not tutoring is effective in boosting academic performance.

Function

SQL: Find list of tutoring hours and matching average exam grade.

Matlab: Find correlation between tutoring hours and academic performance.

SQL: Find list of tutoring hours and matching average exam grade.

SELECT *FROM (SELECT Avg(Grade.Score)

AS [Exam Grade], Count(TutorHours.SID) AS [Tutoring Amount] FROM [After school Tutoring] AS TutorHours, [In Class Exam Grade] AS Grade WHERE (TutorHours.SID) In (SELECT of_SID FROM [In Class Exam Grade]) GROUP BY Grade.of_SID)

WHERE [Tutoring Amount] > 0;


Query 1

Matlab: Find correlation between tutoring hours and academic performance.

f = csvread('acadPerf.csv');x = f(:,1);y = f(:,2);hold onscatter(x,y)

p = polyfit(x,y,1);r = p(1) .* x + p(2);plot(x,r)xlabel('Tutoring Hours');ylabel('Average Test Score');title('Tutoring on Academic Performance');


Query 1

Query 2

How should the cafeteria administration forecast demand?


Query 2Purpose

Minimize the waiting time in the cafeteria and predict the order quantity of meal type.

Function

SQL: Find mean and standard deviation from data

Newsvendor Problem: Find optimal order quantity for a given type of meal.

Square Root Law: Find number of servers to attain a given level of service

Approximated Waiting Time in Line Formula: Find ideal number of servers for 500 students.


Query 2SQL: Find demand of different cafeteria foods

using mean and standard deviation.

SELECT [Cafeteria Order].MealName, Avg([Cafeteria Order].MealOrderQuantity) AS AvgOfMealOrderQuantity, StDev([Cafeteria Order].MealOrderQuantity) AS StDevOfMealOrderQuantity

FROM [Cafeteria Order]GROUP BY [Cafeteria Order].MealName;

Assumption:1. Selling price = $42. Purchase price = $23. Salvage value = $0.50. Assuming that the school will resell the leftover

food.So, we find:4. Overage cost: Co = $2-0.5 = $1.505. Underage cost: Cu = 4-2 = $2

F(Q*) = = 0.57

Then, find Z value from the table using the mean and the standard deviation.

For example, if the school wants to find out how many pasta to order, they have to calculate:

Q* = 114.25 + 18.89 * 0.57 = 117.58.Therefore, the optimal order quantity is 117.58 servings of pasta.


Query 2

Cu

Cu + Co

Newsvendor Problem

Square root law

Assumptions: All students have a lunch break is 45 minutes. We assume that all students grab lunch in the first 15 minutes because they need time to finish their lunches. The lambda=25 students/minute and mu=4 students/ minute where lambda is the arrival rate of the students in the cafeteria and mu is the service rate of the cafeteria staff. We also assumed that there is 1 line because 1 line allows for greater utilization of all s servers.

Basic equation for number of servers to have to attain a given level of service:

N=R+BR.5

N = the number of serversR=(lambda)/(mu)Beta is a parameter related to the service level or P(wait)R = 11/4 = 6.25 N = 6.25+1.7(6.25).5 =10.5


Query 2

Decision analysis method

2 possible choices:1. Having 10 servers

10 = 6.25 + *(6.25)^(.5) => = 1.5ℬ ℬP{waiting} = 0.0668 ≈6.68%

2. Having 11 servers11 = 6.25 + *(6.25)^(.5) => = 1.9ℬ ℬP{waiting} = 0.0287 ≈2.87%

Using formula for approximated waiting time in line

1. Wq = 0.0118 min/students = 0.718 seconds/students2. Wq = 0.0058 min/students = 0.35 seconds/students

Conclusion: Ideally have 10 servers in a situation where there are 500 students.


Query 2

Query 3How can we assign students to

classrooms in the most efficient way?


Query 3

Purpose

Ensure equal gender distribution in each classroom and allow students to flourish in groups with similar academic

performance

Function

SQL: Group students by average exam grade

AMPL: Place students with similar average exam grade into a classroom of 30 students with specific gender distribution


Query 3SQL: Order students by

gender, grade, and average academic grade.

SELECT g.of_SID, Avg(g.Score) AS AvgOfScore, Person.gender AS Gender, s.Year

FROM ([In Class Exam Grade] AS g INNER JOIN STUDENT AS s ON g.of_SID = s.SID) INNER JOIN Person ON s.SSN = Person.SSN

GROUP BY g.of_SID, Person.gender, s.Year, AvgOfScore;


Query 3AMPL: Use SQL query

results to sort students into classrooms.

Data File

• param a:= 45; # number of girls in a particular grade

• param b:= 45; # number of boys in a particular grade

• param n:= 3; # number of classes in a particular grade

AMPL: Set parameters and decision variables for linear programming.

Model File

• param a;

• # number of girls in a particular grade• param b;

• # number of boys in a particular grade• param n;

• # number of classes in a particular grade

• var x{i in 1..a, j in 1..n} binary;

• # 1 if female student i is in class j, 0

o/w• var y{k in 1..b, j in 1..n} binary;

• # 1 if male student k is in class j, 0 o/w

AMPL: Set objective function and constraints.

• minimize students {j in 1..n}: sum{i in 1..a} x[i,j] + sum{k in 1..b} y[k,j];

• subject to maximum_student_per_class {j in 1..n}: • sum{i in 1..a} x[i,j] + sum{k in 1..b} y[k,j] <= 30;

• # Maximum of 30 students per class• subject to minimum_female_per_class {j in 1..n}:

• sum{i in 1..a} x[i,j] >= 13; • # At least 13 girls in each class• subject to minimum_male_per_class {j in 1..n}:

• sum{k in 1..b} y[k,j] >= 13; • # At least 13 boys in each class• subject to female_student {i in 1..a}:

• sum{j in 1..n} x[i,j] = 1; • # Each female student is allowed to be allocated in 1 class• subject to male_student {k in 1..b}:

• sum{j in 1..n} y[k,j] = 1; • # Each male student is allowed to be allocated in 1 class


Query 3


Query 3

Query 4What donating trends can the school take advantage of for fundraising efforts?


Query 4

Purpose

Maximize the school’s revenues from fundraising campaigns.

Function

SQL: Find the addresses where people are most likely to donate.

Python: Map out donations by address and by donation amount.

SQL: Find the times of the year when people are most likely to donate.


Query 4SQL: Find the addresses where people are most likely to donate.

SELECT Person.Address, Person.Zipcode, SUM([Finance Data].FinAmount) AS [Donation Amount]

FROM (([Donation]INNER JOIN [Finance Data] ON [Donation].[FID] =[Finance Data].[FID])INNER JOIN Person ON [Donation].SSN = [Person].SSN)GROUP BY Person.Address, Person.Zipcode;


Query 4

Python:

Map outaddresses that wereextracted fromSQL onto a map in order to visualize which neighborhoods are donating the most amount of money tothe school.


Query 4


Query 4

SQL: Find the months when people are most likely todonate.

SELECT FinName, SUM(f.FinAmount), f.FinDate

FROM Financial-accountWHERE FinType=‘Donation’GROUP BY FinDateSORT BY SUM(FinAmount) DESC;

Query 5

How can the school minimize the cost of purchasing library books?


Query 5

Purpose

Maximize student satisfaction while optimizing use of limited funds and library space.

Function

Java: Derive excess waiting time and purchase or sell additional copies of book accordingly.

Time Value of Money: Find the rate at which the school will need to replace the book.


Query 5 Java: Derive excess waiting time and purchase or sell additional copies of book accordingly


Query 5

Time Value of Money: Find the rate at which the school will need to replace the book.

Assumptions:The school wants to resell books at 40% of the original price

and keep one copy of each book. Assuming the rate of book depreciation is at a constant rate of 1% per week, then the school can calculate how long a book should be kept.

(Original Price) * (1+r)n = (Current Price)

r = (-)1% per week(Original Price) * (1-0.01)n = 40% (Original Price)(0.99)n = 0.4n ln (0.99) = ln (0.4) n = ln (0.4) / ln (0.99)n = 91.2 weeks

NormalizationNormalizationEER Schema QueriesIntro

NormalizationDecomposing from 1NF to 2NF:

Achievement (Type, SID1a, Date, Reward, Comment)

To normalize from 1NF to 2NF:

AchievementInfo (Type, SID1a, Date, Comment)AchievementReward (Type, Reward)

Type SID Date Reward Comment

Decomposing from 1NF to 2NF:

InClassExam (Date, ExamName, Score, Average, Is_in_RID15, Of_SID1a, Scored_bySTID1a)


ExamScore (Date, ExamName, Of_SID1a, Score, Is_in_RID15)InClassExam (Date, ExamName, Average, Scored_by1a)


Normalization

Date ExamName SID Score RID Average Scored_By



Report_Card (RID, SID1a, CourseName, Grade, Comment, Grade_Level, Reported_to_SSN1, Completed_by_SSN1)


Score_Submission (RID, Completed_by_SSN1, Grade_Level, CourseName)ReportCard (RID, SID1a, Comment, Grade, Reported_to_SSN1)

RID SID CourseName Grade Comment Grade_Level Reported_By_SSN Completed_By_SSN



PTA (PTAID, SSN1, Position, VolunteerHours)


PTAOrg (PTAID, Position, VolunteerHours)PTA_Member (PTAID, SSN1)

PTAID SSN Position Volunteer_Hours



PTAOrg (PTAID, Position, VolunteerHours)PTA_Member (PTAID, SSN1)


PTAPositionReq (Position, LeastVolunteerHours)PTAOrg (PTAID, Position)

PTAMember (PTAID, SSN1, VolunteerHours)

PTAID Position Volunteer_Hours

Closing Statements

Q & A

Documents

Buena Vista Elementary School