1. Converting ER/EER to logical schema; physical design issues 1

2. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS Mapping Regular Entities to Relations  Simple attributes: E-R attributes map directly onto the relation  Composite attributes: Use only their simple, component attributes  Multivalued Attribute: Becomes a separate relation with a foreign key taken from the superior entity 2

3. (a) CUSTOMER entity type with simple attributes Figure 4-8 Mapping a regular entity (b) CUSTOMER relation 3 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

4. 4 (a) CUSTOMER entity type with composite attribute Figure 4-9 Mapping a composite attribute (b) CUSTOMER relation with address detail 4 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

5. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall 5 Figure 4-10 Mapping an entity with a multivalued attribute One–to–many relationship between original entity and new relation (a) Multivalued attribute becomes a separate relation with foreign key (b)

6. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Weak Entities  Becomes a separate relation with a foreign key taken from the superior entity  Primary key composed of:  Partial identifier of weak entity  Primary key of identifying relation (strong entity) 6

7. 7 Figure 4-11 Example of mapping a weak entity a) Weak entity DEPENDENT 7 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

8. 8 NOTE: the domain constraint for the foreign key should NOT allow null value if DEPENDENT is a weak entity Foreign key Composite primary key Figure 4-11 Example of mapping a weak entity (cont.) b) Relations resulting from weak entity 8 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

9. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Binary Relationships  One-to-Many–Primary key on the one side becomes a foreign key on the many side  Many-to-Many–Create a new relation with the primary keys of the two entities as its primary key  One-to-One–Primary key on mandatory side becomes a foreign key on optional side 9

10. 10 Figure 4-12 Example of mapping a 1:M relationship a) Relationship between customers and orders Note the mandatory one b) Mapping the relationship Again, no null value in the foreign key…this is because of the mandatory minimum cardinality. Foreign key 10 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

11. 11 Figure 4-13 Example of mapping an M:N relationship a) Completes relationship (M:N) The Completes relationship will need to become a separate relation. 11 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

12. 12 new intersection relation Foreign key Composite primary key Figure 4-13 Example of mapping an M:N relationship (cont.) b) Three resulting relations 12 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

13. 13 Figure 4-14 Example of mapping a binary 1:1 relationship a) In charge relationship (1:1) Often in 1:1 relationships, one direction is optional 13 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

14. 14 b) Resulting relations Figure 4-14 Example of mapping a binary 1:1 relationship (cont.) Foreign key goes in the relation on the optional side, matching the primary key on the mandatory side 14 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

15. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Associative Entities  Identifier Not Assigned  Default primary key for the association relation is composed of the primary keys of the two entities (as in M:N relationship)  Identifier Assigned  It is natural and familiar to end-users  Default identifier may not be unique 15

16. 16 Figure 4-15 Example of mapping an associative entity a) An associative entity 16 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

17. 17 Figure 4-15 Example of mapping an associative entity (cont.) b) Three resulting relations Composite primary key formed from the two foreign keys 17 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

18. 18 Figure 4-16 Example of mapping an associative entity with an identifier a) SHIPMENT associative entity 18 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

19. 19 Figure 4-16 Example of mapping an associative entity with an identifier (cont.) b) Three resulting relations Primary key differs from foreign keys 19 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

20. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Unary Relationships  One-to-Many–Recursive foreign key in the same relation  Many-to-Many–Two relations:  One for the entity type  One for an associative relation in which the primary key has two attributes, both taken from the primary key of the entity 20

21. 21 Figure 4-17 Mapping a unary 1:N relationship (a) EMPLOYEE entity with unary relationship (b) EMPLOYEE relation with recursive foreign key 21 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

22. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall 22 Figure 4-18 Mapping a unary M:N relationship (a) Bill-of-materials relationships (M:N) (b) ITEM and COMPONENT relations

23. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Ternary (and n-ary) Relationships  One relation for each entity and one for the associative entity  Associative entity has foreign keys to each entity in the relationship 23

24. 24 Figure 4-19 Mapping a ternary relationship a) PATIENT TREATMENT Ternary relationship with associative entity 24 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

25. 25 b) Mapping the ternary relationship PATIENT TREATMENT Remember that the primary key MUST be unique. Figure 4-19 Mapping a ternary relationship (cont.) This is why treatment date and time are included in the composite primary key. But this makes a very cumbersome key… It would be better to create a surrogate key like Treatment#. 25 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

26. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall TRANSFORMING EER DIAGRAMS INTO RELATIONS (CONT.) Mapping Supertype/Subtype Relationships  One relation for supertype and for each subtype  Supertype attributes (including identifier and subtype discriminator) go into supertype relation  Subtype attributes go into each subtype; primary key of supertype relation also becomes primary key of subtype relation  1:1 relationship established between supertype and each subtype, with supertype as primary table 26

27. 27 Figure 4-20 Supertype/subtype relationships 27 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

28. 28 Figure 4-21 Mapping supertype/subtype relationships to relations These are implemented as one-to-one relationships. 28 Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

29. CHAPTER 5: PHYSICAL DATABASE DESIGN AND PERFORMANCE © 2013 Pearson Education, Inc. Publishing as Prentice Hall 29 Modern Database Management 11 th Edition Jeffrey A. Hoffer, V. Ramesh, Heikki Topi

30. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall PHYSICAL DATABASE DESIGN  Purpose–translate the logical description of data into the technical specifications for storing and retrieving data  Goal–create a design for storing data that will provide adequate performance and ensure database integrity, security, and recoverability 30

31. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall DESIGNING FIELDS  Field: smallest unit of application data recognized by system software  Field design  Choosing data type  Coding, compression, encryption  Controlling data integrity 31

32. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall CHOOSING DATA TYPES 32

33. 33 Figure 5-2 Example of a code look-up table (Pine Valley Furniture Company) Code saves space, but costs an additional lookup to obtain actual value 33 Chapter 5 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

34. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall HANDLING MISSING DATA  Substitute an estimate of the missing value (e.g., using a formula)  Construct a report listing missing values  In programs, ignore missing data unless the value is significant (sensitivity testing) 34 Triggers can be used to perform these operations.

35. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall DENORMALIZATION  Transforming normalized relations into non-normalized physical record specifications  Benefits:  Can improve performance (speed) by reducing number of table lookups (i.e. reduce number of necessary join queries)  Costs (due to data duplication)  Wasted storage space  Data integrity/consistency threats  Common denormalization opportunities  One-to-one relationship (Fig. 5-3)  Many-to-many relationship with non-key attributes (associative entity) (Fig. 5-4)  Reference data (1:N relationship where 1-side has data not used in any other relationship) (Fig. 5-5) 35

36. 36 Figure 5-3 A possible denormalization situation: two entities with one- to-one relationship 36 Chapter 5 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

37. 37 Figure 5-4 A possible denormalization situation: a many-to-many relationship with nonkey attributes Extra table access required Null description possible 37 Chapter 5 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

38. 38 Figure 5-5 A possible denormalization situation: reference data Extra table access required Data duplication 38 Chapter 5 © 2013 Pearson Education, Inc. Publishing as Prentice Hall

39. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall DENORMALIZE WITH CAUTION  Denormalization can  Increase chance of errors and inconsistencies  Reintroduce anomalies  Force reprogramming when business rules change  Perhaps other methods could be used to improve performance of joins  Organization of tables in the database (file organization and clustering)  Proper query design and optimization 39

40. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall PARTITIONING  Horizontal Partitioning: Distributing the rows of a logical relation into several separate tables  Useful for situations where different users need access to different rows  Three types: Key Range Partitioning, Hash Partitioning, or Composite Partitioning  Vertical Partitioning: Distributing the columns of a logical relation into several separate physical tables  Useful for situations where different users need access to different columns  The primary key must be repeated in each file  Combinations of Horizontal and Vertical 40

41. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall PARTITIONING PROS AND CONS  Advantages of Partitioning:  Efficiency: Records used together are grouped together  Local optimization: Each partition can be optimized for performance  Security: data not relevant to users are segregated  Recovery and uptime: smaller files take less time to back up  Load balancing: Partitions stored on different disks, reduces contention  Disadvantages of Partitioning:  Inconsistent access speed: Slow retrievals across partitions  Complexity: Non-transparent partitioning  Extra space or update time: Duplicate data; access from multiple partitions 41

42. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall RULES FOR USING INDEXES 1. Use on larger tables 2. Index the primary key of each table 3. Index search fields (fields frequently in WHERE clause) 4. Fields in SQL ORDER BY and GROUP BY commands 5. When there are >100 values but not when there are 100 values but not when there are <30 values 42

43. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall RULES FOR USING INDEXES (CONT.) 6. Avoid use of indexes for fields with long values; perhaps compress values first 7. If key to index is used to determine location of record, use surrogate (like sequence number) to allow even spread in storage area 8. DBMS may have limit on number of indexes per table and number of bytes per indexed field(s) 9. Be careful of indexing attributes with null values; many DBMSs will not recognize null values in an index search 43

44. Chapter 4 © 2013 Pearson Education, Inc. Publishing as Prentice Hall RULES FOR USING INDEXES (CONT.) 6. Avoid use of indexes for fields with long values; perhaps compress values first 7. If key to index is used to determine location of record, use surrogate (like sequence number) to allow even spread in storage area 8. DBMS may have limit on number of indexes per table and number of bytes per indexed field(s) 9. Be careful of indexing attributes with null values; many DBMSs will not recognize null values in an index search 44