International Student College Experience
Enhancement Program
Team Members
Alice Zhang
Florence Liao
Huan Guo
Jake Magner
Li Shubin
Viraj Mohan
Zahin Ali
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Project Background
Project Objective
To design a database for a website that helps international
students with various aspects of “settling in”, by providing a
platform for interaction between students, local communities,
cultural organizations and employers
Client
XiYiRen, a start up social utility website will be using a small
part of our expansive project, focusing on Chinese students.
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DP I Summary
Progress
Project Background: Objective
and Client description
Summary of entities involved
Database capabilities
Simplified EER diagram with 10
entities, 3 Weak
entities/relationships, and
superclass/subclass division
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DP II Summary
Progress
Revised simplified EER diagram
Including more entities and 30 relationships
Implementation of queries in relational algebra
Realized need for more complex queries utilizing
IEOR methods: forecasting, optimal event locating, etc.
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DP III Summary
Progress
Revised simplified EER diagram
Relational schema
Five queries implemented in SQL and Access
Focused on client-centric queries
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EER
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Relational Schema
1. Person(Pid, Fname, Lname, MI, Birth_date, Profile5)
2. Student(Pid1, Housing7, University14, Pickup_Person3, Flight, Country11, price_preference,
year, sleep, wakeup, study, friends, outgoing)
3. Community_Member(Pid1, occupation)
4. Alumni(Pid1, Class, Occupation, Donation_Amount)
5. Profile(Profile_id, Pic, Email, Phone)
6. Location(Street, City, State, Apt_Suite, Zip, x, y)
7. Housing(Hid, offered_by_person1, Street6, Apt_Suite6, Zip 6, offered_by_org8, org_profile5,
price, availability_date, furnished, number_rooms, number_bathrooms, water, electice,
garbage, gas, internet, move-in special)
8. Organization(OrgName, Profile_id5, Street6, Apt_Suite6, Zip 6, type, description)
9. Department(DepName, University14)
10. Event(EventName, Profile_id5, Street6, Apt_Suite6, Zip 6, description, attendance, date, time)
11. Country(Name, Capital, Population)
12. Language(Name, Countries_spoken_in)
13. Resource(Rid, Owner1, Price, Quantity)
14. University(Name, student_population, ranking)
15. Donation(Did, Amount, Time, Date, Pid1)
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Relational Schema (contd)
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Mentors(Mentor1, Mentee2)
Student_University(Student2, University14)
Person_in_Org(Person1, OrgName8, OrgProfile5)
RSVP(Person1, EventName10, EventProfile5, SurveyScore)
Student_in_Department(Student2, DepName9, UniName14)
Person_speaks_language(Person1, Language12)
Housing_near_Uni(Housing7, UniName14)
Organization_University(OrgName8, OrgProfile5, UniName14)
Org_holds_event(OrgName8, OrgProfile5, EventName10, EventProfile5)
Org_speaks_Language(OrgName8, OrgProfile5, Language12)
Org_Country(OrgName8, OrgProfile5, Country11)
Dep_sponsors_event(DepName9, UniName14, EventName10, EventProfile5)
Event_speaks_language(EventName10, EventProfile5, Language12)
Event_country(EventName10, EventProfile5, Country11)
Country_Language(Country11, Language12)
Alumni_Uni(Pid4, UniName14, class_of)
Alumni_Dept(Pid4, DepName9)
Person_gives_donation(Pid1, Did15)
Rommates(Pid11, Pid21)
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Relational Design
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Query 1: Roommate Matching
Description
•Shows all possible roommate combinations ordered by MatchRating.
• A dorm/off-campus housing facility can use it to pair up students interested
in their housing
Description of Attributes
Sleep
Early to late sleep time (Scale of 1-5)
Wakeup
Early to late (1-5)
Outgoing
Outgoingness Level (1-5)
Study
In room(1) - Library(5)
Friends
INTRODUCTION
Having friends in room never(1) –
always(5)
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Query 1: Roommate Matching
SQL Code
SELECT P.Fname, P.Lname, Q.Fname, Q.Lname, Min(0.2*(Abs(S.sleepR.sleep))+0.2*(Abs(S.wakeup-R.wakeup))+0.2*(Abs(S.outgoingR.outgoing))+0.2*(Abs(S.study-R.study))+0.2*(Abs(S.friendsR.friends))) AS Matchrating
FROM Student AS S, Student AS R, Person AS P, Person AS Q
WHERE (((S.pid)=[P].[pid]) AND ((Q.pid)=[R].[pid] And (Q.pid)<[P].[pid]))
GROUP BY P.Fname, P.Lname, Q.Fname, Q.Lname
HAVING (((([P].[Fname]=[Q].[Fname]) And ([P].[Lname]=[Q].[Lname]))=False))
ORDER BY Min(0.2*(20-Abs(S.sleep-R.sleep))+0.2*(20-Abs(S.wakeupR.wakeup))+0.2*(20-Abs(S.outgoing-R.outgoing))+0.2*(20Abs(S.study-R.study))+0.2*(20-Abs(S.friends-R.friends)));
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Query 1: Roommate Matching
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Query 2: New Student Forecasting
Description
•Extracts the data of how many new students come each year which can then
be used to forecast the future number of students
•The year table is a one attribute table containing a list of years
•Uses regression equation y=ax+b with slope b = (N∑XY - (∑X)(∑Y))/(N∑X2 (∑X)2), and intercept a = (∑Y - b(∑X))/N. Where N = number of tuples, X =year,
and Y = number of students
SQL Code
SELECT y.year AS [Year], count(s.pid) AS Number_Of_Students, u.name AS
University
FROM [year] AS y, student AS s, university AS u
WHERE s.year=y.year AND s.university=u.name
GROUP BY y.year, u.name
ORDER BY y.year;
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Query 3: Event Interest
Description
•Outputs a list of all events along with their computed attendance rate, the
average level of student interest, and a metric combining surveyed interest
with actual attendance
•Organizations throwing events with low attendance but high survey scores
may need to look into changing venues or increasing advertising.
SQL Code
SELECT e.EventName, e.Attendance/(Count(r.person)) AS Attendance_Rate,
Avg(r.SurveyScore) AS Surveyed_Interest,
Avg(r.SurveyScore)*e.Attendance/(Count(r.person)) AS Interest_Metric
FROM Event AS e, RSVP AS r
WHERE (((r.EventProfile)=[e].[Profile_id]))
GROUP BY e.EventName, e.Profile_id, e.Attendance
ORDER BY Avg(r.SurveyScore)*e.Attendance/(Count(r.person)) DESC;
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Query 3: Event Interest
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Query 4: Optimal Event Location
Description
•Selects optimal potential event location on UC Berkeley campus in relation to
attendee housing locations.
•By utilizing P-Median approach for event location that minimizes total
demand weighted distances
•Assume P = 1 and calculate Dij by utilizing Euclidean distance formula:
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Query 4: Optimal Event Location
SQL Code
SELECT e.EventName, l2.street AS Potential_Location, sum(((l.x-l2.x)^2)+((l.yl2.y)^2)^0.5) AS distance, AVG(s.EventInterest) AS Demand
FROM Student AS s, RSVP AS p, Housing AS h, location AS l, location AS l2,
Event AS e
WHERE s.PID=p.person And p.EventName=e.EventName And s.housing=h.hid
And h.street=l.street And h.state=l.state And h.city=l.city And
h.apt_suite=l.apt_suite And h.zip=l.zip
GROUP BY e.EventName, l2.street
ORDER BY e.EventName, sum(((l.x-l2.x)^2)+((l.y-l2.y)^2)^0.5);
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Query 4: Optimal Event Location
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Query 5: Min Airport Pick-up Cost
Description
Assumptions:
(1)Only take students who arrive at the airport between 8am to 7:59 pm into account
(2)Buses leave the airport on the hour.
(3)The opportunity cost of each student waiting for a bus for an hour is $10. (4) Each
type I bus has a total of 5 seats and each type II bus has a total of 10 seats.
(5) We only deal with the arrival hour of each student, (student arriving at 1:01pm is
treated the same as a student arriving at 1:59pm in this query implementation. and a
ten-seat-vehicle to the airport and back cost $50 and $100, respectively.
•For date, airport extract # of students arriving in each time interval Ci
•A≤i≤L; Ci is interpreted as the number of students arriving at the airport no earlier than
(i-1) o’clock but prior to i o’clock
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Query 5: Min Airport Pick-up Cost
Formulation
Decision variables:
Subject to.
tij= 1 if a type j bus is arranged to People_constrain {Z in
pick up students at i o’clock.
A,B,C,D,E,F,G,H,I,J,K,L}:
tij = 0 otherwise; (For A≤i≤L, 1≤j≤2)
Objective Function (Cost Min.):
SQL Code
SELECT s.airport AS Airport, s.arr_date AS Arr_Date, s.flight_arr_hour AS
Arr_Time, COUNT(*) AS Number_of_Students
FROM student AS s
GROUP BY s.flight_arr_hour, s.arr_date, s.airport;
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Query 5: Min Airport Pick-up Cost
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Normalization Analysis: 1NF
1NF
R is in 1NF if the domain of an attribute must include only atomic
(simple, indivisible) values and that the value of any attribute in a
tuple must be a single value from the domain of that attribute.
Profile (Profile_id, Pic, Emails, Phones)

Pic (Profile_id, Pic)
Email (Profile_id, Email)
Phone (Profile_id, Phone)
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Normalization Analysis: 2NF
2NF
R is in 2NF if R is in 1NF and every nonprime attribute A in R is fully
functionally dependent on the primary key of R.
Location (Street, City, State, Apt_Suite, Zip, x, y)
Assumption: ZIP_CODE determines CITY and STATE.

Location1 (Street, Apt_Suite, Zip, x, y)
Zip (Zip, City, State)
Organization (OrgName, Profile_id5, Street6, Apt_Suite6, Zip6, type,
description)
Assumption: The name of an organization determines its type.

OrgName (OrgName, Type)
Organization1 (OrgName, Profile_id5, Street6, Apt_Suite6, Zip6,
description)
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Normalization Analysis: 3NF
3NF
R is in 3NF if R is in 2NF and no nonprime attribute of R is transitively
dependent on the primary key.
Housing (Hid, offered_by_person1, Street6, Apt_Suite6, Zip 6,
offered_by_org8, org_profile5, price, availability_date, furnished,
number_rooms, number_bathrooms, water, electricity, garbage, gas,
internet, move_in_special, ready_to_move_in)
Assumption: For a housing place to be “ready to move in”, it has to have
Internet, water, electricity, gas and garbage.

Housing1 (Hid, offered_by_person1, Street6, Apt_Suite6, Zip 6,
offered_by_org8, org_profile5, price, availability_date, furnished,
number_rooms, number_bathrooms, move_in_special, Water, Electricity,
Garbage, Gas, Internet)
Ready_to_move_in (ready_to_move_in, Water, Electricity, Garbage, Gas,
Internet)
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Normalization Analysis: BCNF
BCNF
R is in BCNF if whenever a nontrivial functional dependency XA
holds in R, then X is a superkey of R.
Student (Pid1, Housing7, University14, Pickup_Person3, Flight,
Country11, price_preference, year, sleep, wakeup, study, friends,
outgoing)
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Organization Form
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Person Form
Student Report
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Questions?