presents

Angela Carlin

Thomas Choi

Matthew Hedges

Matthew Iong

Harsh Karmarkar

David Ng

Ryan Salcedo

A++ ConsultingOur Team

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Executive Summary

•Company Review

•EER Diagram

•Verbal Explanation of Queries

•Implementation in Access

•Q & A

•Demand driven online publication

•Industry papers reviewed and published

•Editors around the globe

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PUBLISHED

TOR_ACCOUNTS

INSTITUTION

READER

EDITOR

PAPER

WORKING

UNDER REVIEW

KEYWORD

ADMINISTRATOR

USER

o

UNREGISTERED

Views

SUBJECT

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TOR EER Diagram

Belong_To

(0,N)

(0,M)

(0,M)

Monitors_Acct

(1,M)

(1,N)

Monitors_User

(1,M)

(1,N)

Subscribes

(0,N)

(0,1)

Donates_To

(0,1)

(0,N)

Has

(0,M)

(1,N)

AUTHORE

OUTSIDE_PAPER

Reference_Outside

(0,M)

(1,N) Has

(0,M)

(0,N)

Monitors_Paper

(1,N)

(0,M)

Is_On

(0,M)

(1,N)

References_Internal

(0,M)

(1,N)

Submits

(0,M)

(1,N)

Reviews(0,M)

(0,N)

Discusses

(1,N)

(0,M)

Accepts

(0,N)

(1,1)

(0,N)

d

Query 1 – Financial Solicitation

Purpose:

Gives TOR an idea of how much money they can expect to receive from a particular institution should they request a donation from that institution

Application:

1) TOR will be able to target the most generous institutions in the future for financial aid.

2) Also, TOR can filter out the institutions that are expected to give the lowest donations and pursue them more aggressively in order to receive more donations.

Query 1 – Financial Solicitation

SQL (4 sections):

TOR_Avg

SELECT AVG(DT.Amount) AS TOR_Avg FROM Donates_To AS DT;

================================ All_Individual_Donations

SELECT DT.SponsorID AS SponsorID, COUNT(DT.SponsorID) AS Num, AVG(DT.Amount) AS Avg_Donation

FROM Donates_To AS DT GROUP BY [SponsorID]; ================================

Qualified_Donors

SELECT * FROM All_Individual_Donations WHERE Num>2;

Query 1 – Financial Solicitation

Expected Donations SELECT DISTINCT DT.SponsorID AS SponsorID, I.InstitutionName AS

Name,((QD.Num*QD.Avg_Donation)/(QD.Num+2))+((2*TA.TOR_Avg)/(QD.Num+2)) AS Weighted_Expected_Donation

FROM Donates_To AS DT, Institution AS I, Qualifed_Donors AS QD, TOR_Avg AS TA

WHERE (QD.SponsorID=DT.SponsorID And I.InstitutionID=DT.SponsorID And QD.SponsorID=I.InstitutionID);

Query 1 – Financial Solicitation

Purpose:

Returns the papers, grouped by their subject, that have been referenced the most by other papers.

Query 2 – Most Referenced Papers

Application:

1) Allows TOR to track papers that contain the most important, useful content

2) Helps TOR determine which topic is gaining momentum and is widely discussed in the industry.

Query 2 – Most Referenced Papers

SQL: SELECT S.Field, P.Title, COUNT(RI.Referencing_PID) AS

Num_of_Times_Referenced FROM Paper AS P, References_Internal AS RI, Subject AS S, Is_On AS

IO WHERE (P.PID=RI.Referenced_PID And P.PID=IO.PID And

S.SubjectID=IO.SubjectID) GROUP BY S.Field, P.Title

ORDER BY Num_of_Times_Referenced DESC;

Query 2 – Most Referenced Papers

Purpose:

Returns a list of users ranked by the number of times their ratings lie outside of the 90 percent confidence interval for each paper’s rating.

Query 3 – User Bias

Application:

Enables TOR to identify and notify users that regularly give ratings that vary significantly from the norm

Query 3 – User Bias

SQL (3 sections):

Ratings_Stats

SELECT DISTINCT R.WorkingID, STDEV(R.InsightRating+R.ReadibilityRating) AS Rating_STD,AVG(R.InsightRating+R.ReadibilityRating) AS Avg_Rating

FROM Reviews AS RGROUP BY R.WorkingID;

================================ Biased_Reviews

SELECT R.ReaderID AS ReaderID, COUNT(R.ReaderID) AS Biased_ReviewsFROM Ratings_Stats AS RS, Reviews AS RWHERE (ABS(R.InsightRating+R.ReadibilityRating-

RS.Avg_Rating)>(1.25*RS.Rating_STD) And (R.WorkingID=RS.WorkingID))

GROUP BY R.ReaderID;

Query 3 – User Bias

SQL (continued):Biased_Reviewers(#3)

SELECT DISTINCT BR.ReaderID AS ReaderID, U.Fname AS Fname, U.Lname AS Lname, U.Email AS Email,BR.Biased_Reviews

FROM [User] AS U, Institution AS I, Biased_Reviews AS BR, Belongs_To AS BT

WHERE (BR.ReaderID = U.UserID)ORDER BY BR.Biased_Reviews DESC;

Query 3 – User Bias

Purpose:

Returns a distribution that illustrates how long it takes for a paper to be published once submitted

Query 4 – Time Until Publication

Application:

1) TOR can better evaluate its publishing process

2) Show prospective authors approximate timetable if they

submit a paper

Query 4 – Time Until Publication

SQL:

SELECT DATEDIFF (“y”, P.DateSubmitted,Pu.DatePublished) AS Time_as_working_paperFROM Published AS Pu, Paper AS PWHERE P.PID = Pu.PublishedPaperID;

Query 4 – Time Until Publication

Query 4 – Time Until Publication

Purpose:

Forecasts the number of papers that will be submitted in the upcoming month for each subject, using an exponential smoothing model

Query 5 – Paper Forecasts

Application:

1) Gives TOR a better grasp of underlying trends in the

industry 2) Gives TOR understanding of

which topics are the most popular among its readers

Query 5 – Paper Forecasts

SQL (3 Sections): LP_Query SELECT S.SubjectID, COUNT(P1.PID) AS Val

FROM Paper AS P1, Is_On AS O, Subject AS S WHERE ((P1.DateSubmitted Between #1/1/1998# And #12/31/1998#) And P1.PID=O.PID And O.SubjectID=S.SubjectID) GROUP BY S.SubjectID;

================================ CP_Query

SELECT S.SubjectID, COUNT(P1.PID) AS Val FROM Paper AS P1, Is_On AS O, Subject AS S WHERE ((P1.DateSubmitted Between #1/1/1999# And #12/31/1999#) And P1.PID=O.PID And O.SubjectID=S.SubjectID)

GROUP BY S.SubjectID;

Query 5 – Paper Forecasts

SQL ( continued):

Forecasting Papers (#5)

SELECT DISTINCT S.Field, LP.Val AS Last_Period_Total, CP.Val AS This_Period_Total, 0.6*CP.Val+(1-0.6)*LP.Val AS

Next_Period_Forecast FROM Subject AS S, CP_Query AS CP, LP_Query AS LP WHERE S.SubjectID=CP.SubjectID;

Query 5 – Paper Forecasts

Data Entry Forms

Search Forms

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