Client Background

• The Lawrence Hall of Science (LHS) is UC Berkeley’s public science center to provide the general public with hands-on exhibits

• Mission to inspire and foster learning of science and math

• Composed of general exhibits, special programs, public programs, and workshop classes

The Lawrence Hall of Science

Objectives / Implementations

• Organize data more efficiently• Implement data quickly and easily• Pull data from previous records to track

attendance and profits for specific events

LHS objectives

• Organize tables and relationships• Create Access forms and reports• Determine optimal queries to increase

museum sales and visitor attendance, improve operation efficiency, and reduce museum costs

Our objectives

EER Diagram

Relational Design

1.       Admission(VID8, Public_Program16, Admission_Type2, Public_Program_Supplement)2.       Admission_Details(Type, Price)3.       Category(CID, Category_Name, Description)4.       Hired_Workshop(GID8a, WID21, Day_Contracted9, Month_Contracted11,

Year_Contracted)5.       Employee(SSN, First_Name, Last_Name, Address, Phone_Number, Start_Date,

PTO_to_date)a.       Director(DSSN5, Department, Annual_Wage)b.      Facilitator(FSSN5, Days_Available20, Hourly_Wage)c.       Cashier(CSSN5,  Days_Available20)d.      Instructor(ISSN5, Age_Group, Class/Camp, Annual_Wage)

6.       Exhibit(EID, Category3, Exhibit_Name, Organizer, Location, Start_Date, End_Date, Description)7.       Gift_Shop_Purchase(Product15, VID8, Price, Quantity, Date_Bought)8.       Visitor(VID, Date_Visited)

a.       Group(GID8, Group_Name, Size, Interest)i.      Non-School_Group(GID8a)ii.      School_Group(GID8a)

b.      Individual(IID, VID8, First_Name, Last_Name, DOB, Student)i.      Member(MID, IID8b, Expiration_Date9,

Expiration_Month11, Expiration_Year, Start_Day9, Start_Month11, Start_Year,

Sex, Address, Phone_Number, Payment_Type, Disabled_Person, Member_Type10)

ii.      Non-Member(VID8, Payment_Type, Events, Guest_of_MID8bi)

Relational Design

Relational Design

9.       List_of_Days(Day)10.     Membership_Details(Type, Cost, Member_Cards_Allotted, Guest_Passes_Allotted, Planetarium_Passes_Allotted, Parking_Passes_Allotted, Gift_Offered, Facility_Rental_Discount, Special_Events_Invitation)11.   Months_of_the_Year(Month #, Month_Name)12.   Order(SSN5, OID13, Day_Placed9, Month_Placed11, Year_Placed)13.   Order_Details(OID, Size, Amount, Payment_Method, Date_Received)14.   Order_History(OID13, Product15)15.   Product(PID, Product_Name, Age_Range, Category3, Description)16.   Public_Program(PPID, Public_Program_Name, Start_Date, End_Date, Exhibit6,

Admission_Cost)17.   Special_Program(SPID, Exhibit6, Special_Program_Name, Start_Date, End_Date)18.   SuppliedBy(Product15, SID19, Ship_Date, Unit_Cost, Quantity)19.   Supplier(SID, Supplier_Name, Address, Phone_Number, Supplier_Rep, E-mail)20.   Work_Days/Times(Day & Time)21.   Workshop(WID, Age_Group, Type, ISSN)

Relational Design

Relationship View

Query Design#1

1.) What is the economic ordering quantity (EOQ) for a product type? What is the reorder point assuming a continuous review policy and a 95% service level?

• Calculate the demand per week by summing the quantity of a particular product sold over a start and end date length divided by the period

• Use the lead time and holding cost inputs from the user to calculate the EOQ.

• Use the average aggregate function to calculate average demand and use the given holding cost and lead time inputs to calculate the reorder point

Query 1

Implementation

Query Design#1

SELECT DISTINCT p.pid, sqr((2*(sum(b.size)/datediff('ww',[Enter start date],[Enter end date])*[Enter Fixed Cost per order]))/[Enter holding cost]) AS ["Reorder Quantity"], [Enter Lead Time]*avg(b.size)/datediff('ww',[Enter start date],[Enter end date])+1.69*(stdev(b.size))*sqr([Enter Lead Time]) AS ["Reorder Point"]FROM [order details] AS b, product AS p, [order history] AS rWHERE b.oid=r.oid AND r.product=p.pidGROUP BY p.pid;

SQL

Query Design#1

• Improve the LHS inventory management policy

• Determine the optimal ordering quantity based on past demand and inform them of at which inventory level they should place an order

• Reduce costs in terms of holding and ordering costs while still maintaining a high service level.

Significance

Query Design#2

2.) Forecast the number of workshop attendees by quarter, normalized for seasonality.

• Use Winter’s Method to incorporate seasonality

• Initialize the data based on years 2009 and 2010 to forecast for 2011.

• Split up a year into 4 quarters because monthly variation is not large enough to warrant further sectioning of the year

• Find the seasonal factors • Generate the 2011 forecast for each

quarter.

Query 2

Implementation

Query Design#2

(…)SELECT [Workshop Quarters].Quarter, Count([Workshop Quarters].GID) AS [NumGroups that Attended Workshop], [Workshop Quarters].[Year Contracted]FROM (SELECT [Hired Workshop].GID, [Hired Workshop].[Year Contracted], IIf([Hired Workshop]![Month Contracted]<=3,1,IIf([Hired Workshop]![Month Contracted]>3 AND [Hired Workshop]![Month Contracted]<=6,2,IIf([Hired Workshop]![Month Contracted]>6 AND [Hired Workshop]![Month Contracted]<=9,3,4))) AS Quarter FROM [Hired Workshop])  AS [Workshop Quarters]GROUP BY [Workshop Quarters].[Year Contracted], [Workshop Quarters].Quarter;

SQL

Query Design#2

• Forecasts the workshop attendance for the next year, divided into 4 quarters, by incorporating seasonality.

• Better allocate resources depending on demand forecasts.

Significance

Query Design#3

3.) Calculate the return on investment for memberships per year.

• Determine number of sales of a membership type in a year and multiply it by its unit price

• Subtract the sum of the product of the total number of guests brought and the unit price of admission and the product of the number of times a discount is used and unit price of admission

• Divide this difference by the sum

Query 3

Implementation

MembershipRev–

(LossFromMembershipDiscount+LossFromGuestDiscount)(LossFromMembershipDiscount+LossFromGuestDiscount)

Query ROI Formula

Query Design#3

(…)SELECT (([Membership Revenue]![Membership Revenue]-([Loss from Members Using Membership]![Amount of Loss]+[Loss from Members Bringing Guests]![Amount of Loss]))/([Loss from Members Using Membership]![Amount of Loss]+[Loss from Members Bringing Guests]![Amount of Loss])) AS ROI, [Loss from Members Using Membership].Year AS [Year] FROM ([Loss from Members Bringing Guests] INNER JOIN [Loss from Members Using Membership] ON [Loss from Members Bringing Guests].Year = [Loss from Members Using Membership].Year) INNER JOIN [Membership Revenue] ON ([Loss from Members Bringing Guests].Year = [Membership Revenue].[Start Year]) AND ([Loss from Members Using Membership].Year = [Membership Revenue].[Start Year]);

SQL

Query Design#3

• See which years provided the highest return on investment based on current price and usage

• Adjust membership pricing levels and benefits to maximize revenue

Significance

Query Design#4

4.) Rank categories by an “interest factor” based on number of items bought, groups interested, and visitors during an exhibit for a specific category.

• Rank by the number of visitors that visited while an exhibit of that category was on display.

• Rank by the number of items bought of that category

• Rank by the number of groups interested in that category

Query 4

Implementation

.4*NumberOfGroupsInterested+.4*NumberOfVisitors+.2*NumberOfItemsBought

***prioritize higher attendance (number of groups and visitors)

Interest Factor Formula

Query Design#4

(…)SELECT fa.cid, (.4*fa.NumOfVisitors+.4*q2.Group_Quantity+.2*q1.Buys_Quantity) AS Interest_FactorFROM [Category Rank by Visitors] AS fa, [Category Rank by Items] AS q1, [Category Rank by Groups] AS q2WHERE fa.cid=q1.cid and q1.cid=q2.cidORDER BY (.4*fa.NumOfVisitors+.4*q2.Group_Quantity+.2*q1.Buys_Quantity) DESC;

SQL

Query Design#4

• See which categories are most appealing to groups and visitors in terms of both attendance and gift purchases

• Plan future exhibits in categories that will attract the most interest

Significance

Query Design#5

5.) Determine the earliest day when a given exhibit falls below the expected visitor attendance value determined by the client in a preselected date range.

• Determine the number of visitors per day for a client selected exhibit.

• Calculate a moving average for past 5 days. • Select the earliest date where the average

number of visitors fall below a client selected value in a client selected date range.

• Record the earliest date where an exhibit is not as visited as client inputted data.

Query 5

Implementation

Query Design#5

(…)SELECT dc.[DateVisited] as MADate, Avg(dc.NumOfVisitors) as MovingAverage INTO MovingAverageFROM DailyCount as dc, DailyCount as rtWHERE (dc.[DateVisited]>=dc.[StartDate]+5) AND dc.[DateVisited] >= rt.[DateVisited]-5 AND dc.[DateVisited] <=  rt.[DateVisited]GROUP BY dc.[DateVisited]ORDER BY dc.[DateVisited]

SELECT Min(ma.[MADate]) AS EarliestQuit INTO EarliestQuitFROM MovingAverage AS maWHERE ma.[MovingAverage] < [Enter Minimum Visitor Count] AND (ma.[MADate] >= [Enter Search Start Date]) AND (ma.[MADate] <= [Enter Search End Date])

SQL

Query Design#5

• Determine the optimal number of days to display an exhibit based off attendance

• Reduce costs and tailor their exhibits to attract the most number of individuals

Significance

Normalization Analysis

Individual(IID, VID8, First_Name, Last_Name, DOB, Student)Functional Dependencies: IID {VID, First_Name, Last_Name, DOB, Student}1NF: VID is a multi-valued attribute Individual(IID, First_Name, Last_Name, DOB, StudentIndividual_Visits(IID, VID8)2NF: There are no partial dependencies and no composite keys3NF: No non-prime attributes of either relation are transitively dependent on the primary keyBCNF: All attributes are functionally dependent on a super key

Normalization 1

SuppliedBy(Product15, SID19, Ship_Date, Unit_Cost, Quantity)Functional Dependencies: {Product, SID} Unit_Cost {Product, SID, Ship_Date} {Quantity, Unit_Cost}1NF: No multiple values attributes and all attributes are atomic2NF: Unit_Cost was only partially dependent on the primary key.SuppliedBy1(Product, SID, Unit_Cost)SuppliedBy2(Product, SID, Ship_Date, Quantity)3NF: No non-prime attributes of either relation are transitively dependent on the primary key.BCNF: All attributes are functionally dependent on a super key

Normalization 2

Normalization Analysis

Admission(VID8, Admission_Type, Public_Program16, Public_Program_Supplement, Price)Functional Dependencies: VID {Admission_Type, PublicProgram, Public_Program_Supplement, Price} Admission_Type Price PublicProgram Public_Program_Supplement1NF: No multiple valued attributes and all attributes are atomic.2NF: No partial dependencies and no composite keys. 3NF: Public_Program_Supplement was transitively dependent on the primary key in Admission1 in 2NF.Price also transitively dependent on VID through Admission_Type. Admission1(VID8, Public_Program16)Admission2(Admission_Type, Price)Admission3(PublicProgram16, Public_Program_Supplement)BCNF: All attributes are functionally dependent on a super key

Normalization 3

Normalization Analysis

Supplier(SID, Supplier_Name, Address, Phone_Number, Supplier_Rep, E-mail)Functional Dependencies: SID {Supplier_Name, Address, Phone_Number, Supplier_Rep, E-mail} Supplier_Rep E-mail1NF: No multiple valued attributes and all attributes are atomic.2NF: No partial dependencies or composite keys3NF: No non-prime attributes are transitively dependent on the primary keyBCNF: E-mail is not dependent on a super key (Supplier_Rep is not part of candidate key).Supplier(SID, Supplier_Name, Address, Phone_Number, Supplier_Rep)Rep(Supplier_Rep, E-mail)

Normalization 4

Order_Details(OID, Size, Amount, Payment_Method, Date_Received)Functional Dependencies: OID {Size, Amount, Payment_Method, Date_Received}Cannot be further normalized (already in BCNF). All attributes dependent on OID, the candidate key.

Normalization 5

Future Work

• Create additional queries• Track the success of special events

such as Speaker Series• Compare attendances of events

between years. • Track ticket price changes’ effect on

visitor attendance• Compare tickets prices and number

of museum visitors• Drive implementation of current queries

• Organize collected data and input into Access

Future Work

Q & A

Questions?