Chapter 9 Normalizing Database Designs

1. Chapter 9 Normalizing Database Designs Database Principles: Fundamentals of Design, Implementation, and ManagementTenth Edition

2. NORMALIZATION • What is normalization? • Normalization is a procedure that is used to eliminate data redundancy while ensuring data integrity. • Data integrity : ensures the quality of the data in the database • (satisfies data constraint rules. i.e. Primary Key, • Foreign Key, etc.) • Data redundancy: if data can be found in two places in a single database (direct redundancy) or calculated using data from different parts of the database (indirect redundancy) then redundancy exists. • The goal is to remove redundancy and other data modification (insertion, update and deletion) problems

3. What is Data redundancy? • When a group of attributes has multiple values then we say there is a repeating group of attributes in the relation Example: Movie REPEATING GROUP

4. Problems of redundancy • If redundancy exists then this can cause problems during normal database operations: • Updates - when some data needs altering, we find it's in several places. • Insertion - When we want to insert something, we can't because some other data is also required which we don't have. • Deletion - When something is deleted, other data is lost as well.

5. Integrity Constraints An integrity constraint is a rule that restricts the values that may be present in the database. Entity Integrity: The rows (or tuples) in a relation represent entities, and each one must be uniquely identified. Hence we have the primary key that must have a unique not-null value for each row. Referential Integrity: This constraint involves the foreign keys. Foreign keys tie the relations together, so it is vitally important that the links are correct. Every foreign key must either be null or its value must be the actual value of a key in another relation.

6. Entity Integrity: Selecting Primary Keys • Primary key is the most important characteristic of an entity • Single attribute or some combination of attributes • Primary key’s function is to guarantee entity integrity • Primary keys and foreign keys work together to implement relationships • Properly selecting primary key has direct bearing on efficiency and effectiveness

7. Natural Keys and Primary Keys • Natural key is a real-world identifier used to uniquely identify real-world objects • Familiar to end users and forms part of their day-to-day business vocabulary • Generally, data modeler uses natural identifier as primary key of entity being modeled • May instead use composite primary key or surrogate key

8. Primary Key Guidelines • Attribute that uniquely identifies entity instances in an entity set • Could also be combination of attributes • Main function is to uniquely identify an entity instance or row within a table • Guarantee entity integrity, not to “describe” the entity • Primary keys and foreign keys implement relationships among entities • Behind the scenes, hidden from user

10. When to Use Composite Primary Keys • Composite primary keys useful in two cases: • As identifiers of composite entities • In which each primary key combination is allowed once in M:N relationship • As identifiers of weak entities • In which weak entity has a strong identifying relationship with the parent entity • Automatically provides benefit of ensuring that there cannot be duplicate values

12. When to Use Composite Primary Keys(cont’d.) • When used as identifiers of weak entities normally used to represent: • Real-world object that is existent-dependent on another real-world object • Real-world object that is represented in data model as two separate entities in strong identifying relationship • Dependent entity exists only when it is related to parent entity

13. When To Use Surrogate Primary Keys • Especially helpful when there is: • No natural key • Selected candidate key has embedded semantic contents • Selected candidate key is too long or cumbersome

14. When To Use Surrogate Primary Keys (cont’d.) • If you use surrogate key: • Ensure that candidate key of entity in question performs properly • Use “unique index” and “not null” constraints

16. KEY • Key: We can say that one or more attributes of a Relation can be a • Key for that Relation. • Those attributes functionally determine all other attributes of the relation. (There can’t be more than one let say two distinct tuple that is key for a Relation) • No proper subset of the Relation functionally determines all other attributes of Relation. • A key must be minimal. • Super Key: Is an attribute or set of attributes whose closure contains all attributes of the table. (A table may contain many S.K.) • Candidate Key: Is a minimal S.K. (Contains no attributes that are not needed to form a S.K.) (A table may have many C.K.) • Primary Key: The C.K. chosen for implementation. • Each table has only 1 P.K.

17. Example StaffBranch Functional Dependency?Describes the relationship between the attributes in a relation Reasonable Functional Dependencies (FDs): staffNo sName, position, salarybranchNo bAddress So, what is the Key for StaffBranch Relation?

18. Closure Closure of an attribute or set of attributes is the set that contains all attributes that can be determined directly or indirectly by the given attribute or set of attributes. Procedure for Computing Closure of {A1,A2,…..An} START ans={A1,A2,…..An} REPEAT until ans does not change for each FD if the Left Hand Side(LHS) is in ans ADD the Right Hand Side(RHS) to ans STOP Closure is denoted by: { }+

19. Computing Closure Consider the following Relation and FDs: StaffBranch(staffNo,sName,position,salary,branchNo,bAddress) FDs: staffNo sName, position, salary branchNo bAddress Compute the Closures of given attributes: {staffNo}+= {staffNo,sName,position,salary} {branchNo}+= {branchNo,bAddress} {staffNo,branchNo}+={StaffNo,sName,position,salary, branchNo, bAddress}

20. Boyce Codd Normal Form(BCNF) • A table is in BCNF if the determinant of all Functional Dependencies areSuper Key. • Procedure for BCNF: • Step1: Check if the Functional Dependencies violate BCNF. Compute closure of all determinants. Find the Functional Dependencies (F.D.s) that violate BCNF. • Step2: Choose any violating F.D. and form a new table from its closure. List all F.D.s that valid on the new table. Find the Primary Key. • Step3: Eliminate all the non key attributes of the table formed in Step2 from the table that you normalize. Form a new table from the remainders. List all F.D.s that are valid on the new table.

21. EXAMPLES will be solved in the lecture session.

22. Database Tables and Normalization • Normalization • Process for evaluating and correcting table structures to minimize data redundancies • Reduces data anomalies • Series of stages called normal forms: • First normal form (1NF) • Second normal form (2NF) • Third normal form (3NF)

23. Database Tables and Normalization (cont’d.) • Normalization (continued) • 2NF is better than 1NF; 3NF is better than 2NF • For most business database design purposes, 3NF is as high as needed in normalization • Highest level of normalization is not always most desirable • Denormalization produces a lower normal form • Increased performance but greater data redundancy

24. The Need for Normalization • Example: company that manages building projects • Charges its clients by billing hours spent on each contract • Hourly billing rate is dependent on employee’s position • Periodically, report is generated that contains information such as displayed in Table 6.1

26. The Need for Normalization (cont’d.) • Structure of data set in Figure 9.1 does not handle data very well • Table structure appears to work; report is generated with ease • Report may yield different results depending on what data anomaly has occurred • Relational database environment is suited to help designer avoid data integrity problems

27. The Normalization Process • Each table represents a single subject • No data item will be unnecessarily stored in more than one table • All nonprime attributes in a table are dependent on the primary key • Each table is void of insertion, update, and deletion anomalies

29. The Normalization Process (cont’d.) • Objective of normalization is to ensure that all tables are in at least 3NF • Higher forms are not likely to be encountered in business environment • Normalization works one relation at a time • Progressively breaks table into new set of relations based on identified dependencies

31. The Normalization Process (cont’d.) • Partial dependency • Exists when there is a functional dependence in which the determinant is only part of the primary key • Transitive dependency • Exists when there are functional dependencies such that X → Y, Y → Z, and X is the primary key

32. Conversion to First Normal Form • Repeating group • Group of multiple entries of same type can exist for any single key attribute occurrence • Relational table must not contain repeating groups • Normalizing table structure will reduce data redundancies • Normalization is three-step procedure

33. Conversion to First Normal Form (cont’d.) • Step 1: Eliminate the Repeating Groups • Eliminate nulls: each repeating group attribute contains an appropriate data value • Step 2: Identify the Primary Key • Must uniquely identify attribute value • New key must be composed • Step 3: Identify All Dependencies • Dependencies are depicted with a diagram

35. Conversion to First Normal Form (cont’d.) • Dependency diagram: • Depicts all dependencies found within given table structure • Helpful in getting bird’s-eye view of all relationships among table’s attributes • Makes it less likely that you will overlook an important dependency

37. Conversion to First Normal Form (cont’d.) • First normal form describes tabular format: • All key attributes are defined • No repeating groups in the table • All attributes are dependent on primary key • All relational tables satisfy 1NF requirements • Some tables contain partial dependencies • Dependencies are based on part of the primary key • Should be used with caution

38. Conversion to Second Normal Form • Step 1: Make New Tables to Eliminate Partial Dependencies • Write each key component on separate line, then write original (composite) key on last line • Each component will become key in new table • Step 2: Reassign Corresponding Dependent Attributes • Determine attributes that are dependent on other attributes • At this point, most anomalies have been eliminated

40. Conversion to Second Normal Form (cont’d.) • Table is in second normal form (2NF) when: • It is in 1NF and • It includes no partial dependencies: • No attribute is dependent on only portion of primary key

41. Conversion to Third Normal Form • Step 1: Make New Tables to Eliminate Transitive Dependencies • For every transitive dependency, write its determinant as PK for new table • Determinant: any attribute whose value determines other values within a row

42. Conversion to Third Normal Form (cont’d.) • Step 2: Reassign Corresponding Dependent Attributes • Identify attributes dependent on each determinant identified in Step 1 • Identify dependency • Name table to reflect its contents and function

44. Conversion to Third Normal Form (cont’d.) • A table is in third normal form (3NF) when both of the following are true: • It is in 2NF • It contains no transitive dependencies

45. Improving the Design • Table structures should be cleaned up to eliminate initial partial and transitive dependencies • Normalization cannot, by itself, be relied on to make good designs • Valuable because it helps eliminate data redundancies

46. Improving the Design (cont’d.) • Issues to address, in order, to produce a good normalized set of tables: • Evaluate PK Assignments • Evaluate Naming Conventions • Refine Attribute Atomicity • Identify New Attributes

47. Improving the Design (cont’d.) • Identify New Relationships • Refine Primary Keys as Required for Data Granularity • Maintain Historical Accuracy • Evaluate Using Derived Attributes

50. Surrogate Key Considerations • When primary key is considered to be unsuitable, designers use surrogate keys • Data entries in Table 6.4 are inappropriate because they duplicate existing records • No violation of entity or referential integrity