1. Database Design Sections 9 & 10 Modeling Historical Data, conditional nontranferability, time-related constraints 1

2. Modeling Over Time  Any relationship that changes through time needs special consideration.  An attribute such as status may change over time.  Examples of time sensitive attributes are: rental status, fee payment, country name, or price 2

3. Change and Time 1.Every update means loss of information. 2.Time in your model makes the model more complex. 3.There are often complex join conditions. 4.Users can work in advance. 3

4. Why Modeling Time examples  Track trends in products  city police department tracks crimes in each neighborhood frequency of crimes during certain times of the year holidays or during really hot or cold weather 4

5. Entity DAY or Attribute Date  Single attribute entity without M:1 relationships is usually replaced by attribute PURCHASE * date PURCHASE DAY #date on for 5

6. What is wrong with revision? 6

7. Entity Day vs. Attribute Date 7

8. Examples  When there is an interest in a particular day not a date. Is it a holiday? Is it a work day or weekend? Is it leap year? Month end? 8

9. Entity DAY TASK ASSIGNMENT *duration in hours TASK #id DAY #date * public holiday indicator EMPLOYEE #name first day of starts on for of in with 9

10. Modeling Change ASSIGNMENT #start date o end date EMPLOYEE #id COUNTRY #name of for in as 10

11. Modeling Change ASSIGNMENT #start date o end date EMPLOYEE #id COUNTRY #name #start time *end time of for in as life cycle attributes Even a Country has a life cycle 11

12. Mapping Historical Price PRICE = PRICED PRODUCT = HISTORICAL PRICE PRODUCT #id *name PRICE #start date *price $ o end date have for 12

13. Revised previous ERD 13

14. Example Movie Star Jewelry rental JEWELRY PIECE #code *description *rental rate *insured value MOVIE STAR #id * first name * last name rented by renting 14

15. Possible Solution 15

16. 8.1.8 16

17. Review 17

18. Modeling time constraint example 1  Be aware of constraints that can result from the time dimension. Here is an example:  Consider a school fair that features several booths. The manager signs up volunteers to work different shifts at different booths. Some volunteers can work for several hours; others can work fewer hours depending on their free time. The schedule has to be determined in advance, so that the manager knows which times are not covered by any volunteers. 18

19. Review example 1- What are some other rules? 19

20. Example 2 20

21. Modeling change: Price  When do we need to track price? Determine best price for item Sale price vs. original price Grade change Value of home Asking price vs. sale price Stock, bond, or investment Others 21

22. Review slide 4 22

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24. Price with time  Price may fluctuate with time  “The Good Old Days”  Examples: Value of Gold or Silver Real Estate Value of currency Gasoline 24

25. Price with Time PRODUCT #id *name PRICE #start date *price is $ o end date with of Price = Priced Produce = Historical Price 25

26. Slide 11 26

27. Journaling 27

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29. Example  When a student’s grade is changed, we need to record information on the teacher who changed the grade and the reason for the change.  Start with the ENROLLMENT entity, which is the resolution of the M:M between STUDENT and CLASS. 29

30. Solution 10.2.13  STUDENT CLASS ENROLLMENT #date *grade GRADE CHANGE *old grade *changed by #date changed o reason for change Student / Class M:M Enrollment is the intersection entity 30

31. Marge Hohly31 Conventions Review  Crows feet Crows fly East and South Divide complex ERD’s into functional areas Place Highest volume entities in upper left corner Improve readability  avoid criss-crossing lines  increase white spaces so relationships don’t overlap  be consistent with font type, size, and styles 31

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39. Marge Hohly39 Generic Modeling  Can reduce number of entities in diagram  Can provide more flexibility in unstable situations (where business requirements change often)  Use a more distant perspective  Review Slide 3 & 4  See next slide. What would happen to the generic model if we had to add 10 new ARTICLE types, each with their own attributes? 39

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41. Marge Hohly41 Generic Modeling  Have more attributes in fewer entities  Many mandatory requirements/attributes become optional  Structural rules become procedural rules  Example: PANTS waist size was mandatory, with ARTICLE waist size becomes optional  What other businesses would be good candidates for generic modeling? 41

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47. Marge Hohly47 Relational Database Concepts  Conceptual model transforms into a relational database  A relational database is a database that is perceived by the user as a collection of relations or two- dimensional tables.  Table, each employee (instances), and each column (attribute) 47