Post on 20-Dec-2015
presents
Angela Carlin
Thomas Choi
Matthew Hedges
Matthew Iong
Harsh Karmarkar
David Ng
Ryan Salcedo
A++ ConsultingOur Team
A++ Consulting
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
A++ Consulting
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
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