Client Background The Lawrence Hall of Science (LHS) is UC Berkeley’s public science center to...
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![Page 1: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/1.jpg)
![Page 2: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/2.jpg)
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
![Page 3: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/3.jpg)
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
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EER Diagram
![Page 5: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/5.jpg)
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
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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
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Relationship View
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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
![Page 9: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/9.jpg)
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
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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
![Page 11: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/11.jpg)
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
![Page 12: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/12.jpg)
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
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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
![Page 14: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/14.jpg)
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
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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
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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
![Page 17: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/17.jpg)
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
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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
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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
![Page 20: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/20.jpg)
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
![Page 21: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/21.jpg)
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
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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
![Page 23: 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.](https://reader034.fdocuments.net/reader034/viewer/2022051820/56649d2f5503460f94a06fd7/html5/thumbnails/23.jpg)
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
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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
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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
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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
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Q & A
Questions?