INTRODUCTIONDP SUMMARIESQUERIES International Student College Experience Enhancement Program Team...

INTRODUCTION DP SUMMARIES QUERIES

International Student College Experience Enhancement Program

Team MembersAlice ZhangFlorence LiaoHuan GuoJake MagnerLi ShubinViraj MohanZahin Ali

NORMALIZATION

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Project Background

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

Project Objective

XiYiRen, a start up social utility website will be using a small part of our expansive project, focusing on Chinese students.

Client

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Project Background: Objective and Client descriptionSummary of entities involvedDatabase capabilities Simplified EER diagram with 10 entities, 3 Weak entities/relationships, and superclass/subclass division

DP I Summary

Progress


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DP II Summary

Revised simplified EER diagramIncluding more entities and 30 relationships Implementation of queries in relational algebraRealized need for more complex queries utilizing IEOR methods: forecasting, optimal event locating, etc.

Progress

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DP III Summary

Revised simplified EER diagramRelational schema Five queries implemented in SQL and AccessFocused on client-centric queries

Progress


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EER


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Relational Schema1. 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)16. Mentors(Mentor1, Mentee2)17. Student_University(Student2, University14)18. Person_in_Org(Person1, OrgName8, OrgProfile5)19. RSVP(Person1, EventName10, EventProfile5, SurveyScore)20. Student_in_Department(Student2, DepName9, UniName14)21. Person_speaks_language(Person1, Language12)22. Housing_near_Uni(Housing7, UniName14)23. Organization_University(OrgName8, OrgProfile5, UniName14)24. Org_holds_event(OrgName8, OrgProfile5, EventName10, EventProfile5)25. Org_speaks_Language(OrgName8, OrgProfile5, Language12)26. Org_Country(OrgName8, OrgProfile5, Country11)27. Dep_sponsors_event(DepName9, UniName14, EventName10, EventProfile5)28. Event_speaks_language(EventName10, EventProfile5, Language12)29. Event_country(EventName10, EventProfile5, Country11)30. Country_Language(Country11, Language12)31. Alumni_Uni(Pid4, UniName14, class_of)32. Alumni_Dept(Pid4, DepName9)33. Person_gives_donation(Pid1, Did15)34. Rommates(Pid11, Pid21)


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Relational Design


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Query 1: Roommate Matching

•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

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 Having friends in room never(1) – always(5)


FORMS


SELECT P.Fname, P.Lname, Q.Fname, Q.Lname, Min(0.2*(Abs(S.sleep-R.sleep))+0.2*(Abs(S.wakeup-R.wakeup))+0.2*(Abs(S.outgoing-R.outgoing))+0.2*(Abs(S.study-R.study))+0.2*(Abs(S.friends-R.friends))) AS MatchratingFROM Student AS S, Student AS R, Person AS P, Person AS QWHERE (((S.pid)=[P].[pid]) AND ((Q.pid)=[R].[pid] And (Q.pid)<[P].[pid]))GROUP BY P.Fname, P.Lname, Q.Fname, Q.LnameHAVING (((([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.wakeup-R.wakeup))+0.2*(20-Abs(S.outgoing-R.outgoing))+0.2*(20- Abs(S.study-R.study))+0.2*(20-Abs(S.friends-R.friends)));

SQL Code

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Query 2: New Student Forecasting

•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

Description

SELECT y.year AS [Year], count(s.pid) AS Number_Of_Students, u.name AS UniversityFROM [year] AS y, student AS s, university AS uWHERE s.year=y.year AND s.university=u.nameGROUP BY y.year, u.nameORDER BY y.year;

SQL Code


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Query 3: Event Interest

•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.

Description

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_MetricFROM Event AS e, RSVP AS rWHERE (((r.EventProfile)=[e].[Profile_id]))GROUP BY e.EventName, e.Profile_id, e.AttendanceORDER BY Avg(r.SurveyScore)*e.Attendance/(Count(r.person)) DESC;

SQL Code


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Query 3: Event Interest


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Query 4: Optimal Event Location

•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:

Description


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SELECT e.EventName, l2.street AS Potential_Location, sum(((l.x-l2.x)^2)+((l.y-l2.y)^2)^0.5) AS distance, AVG(s.EventInterest) AS DemandFROM Student AS s, RSVP AS p, Housing AS h, location AS l, location AS l2, Event AS eWHERE 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.zipGROUP BY e.EventName, l2.streetORDER BY e.EventName, sum(((l.x-l2.x)^2)+((l.y-l2.y)^2)^0.5);

SQL Code


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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 C i

•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

Description

Query 5: Min Airport Pick-up Cost


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Formulation Decision variables:tij= 1 if a type j bus is arranged to pick up students at i o’clock.tij = 0 otherwise; (For A≤i≤L, 1≤j≤2)Objective Function (Cost Min.):

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 sGROUP BY s.flight_arr_hour, s.arr_date, s.airport;

SQL Code


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Subject to. People_constrain {Z in A,B,C,D,E,F,G,H,I,J,K,L}:



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Normalization Analysis: 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)

1NF


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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)

2NF

INTRODUCTION DP SUMMARIES QUERIES FORMSNORMALIZATION


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)

3NF


Normalization Analysis: BCNF

R is in BCNF if whenever a nontrivial functional dependency XA 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)

BCNF



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Organization Form

Person Form


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Student Report

Questions?

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