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Normalization, Roberts’s Rules and Introduction to Data Modeling

Normalization, Roberts’s Rules and Introduction to Data Modeling

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Normalization, Roberts’s Rules and Introduction to Data Modeling

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  1. Normalization, Roberts’s Rules and Introduction to Data Modeling CSCI 6442

  2. Agenda • Roberts’s Rules • Normalization • Roberts’s Rules and Normalization

  3. Why Are We Talking About This? • To design a database, we choose a set of entities that models a problem • We will store data in tables corresponding to our entity choices • The names of the entity types, and what’s in which table, becomes embedded in our programs • Changing later on is complex, so we want a stable model of the problem

  4. Midterm Question The first question on the midterm will deal with normal forms. It will deal with the relationship between normal forms and Roberts’s Rules. This one question will count more than any other question on the exam. The homework assignment for next week looks a lot like Question 1 on the midterm.

  5. Syntax and Semantics • Syntax deals with the structure and form of a statement or language • Semantics deals with the meaning that is conveyed by a statement or language

  6. Question • Is normalization a syntactic or a semantic construct? • That is, does it deal with the form of information, or is it involved with meaning?

  7. Intentional vs. Extensional Data • Extensional data—the data that is actually present • Intentional data—all the data that is allowed to be present Question: does normalization deal with intentional or extensional data?

  8. Entity and Entity Type • An entity is something that we record information about in the database • An entity type is a set of similar things that we store information about • An entity instance is one example of some entity type. • Usually we don’t say entity instance and entity type when context makes the meaning clear; we just say entity.

  9. Relations • We use a relation to model a single entity type • The relation is a set of tuples • Each tuple is an ordered collection of values of attributes of the entity type • Each tuple of the relation corresponds to a single instance of the entity type

  10. Summary of Terminology

  11. Facts • A value of an attribute in a row conveys one fact about an entity instance • An attribute is a fact stating that “This entity instance has the value <value>” • Consider emp(empno,ename,job,deptno) • Each value of ename in a row states that “This person’s name is <value>”. • Each row of this table can be viewed as a collection of four facts

  12. Example of Facts

  13. Data Modeling • The entire relational database, which is a set of relations, models something in the real world • The job of constructing that set of relations is called data modeling. • In general, in data modeling we are designing a collection of relations that models a part of the real world • All of the formality of normalization is all about how to construct a data model that behaves the way we want it to

  14. What We’ll Do Now • First, we’ll talk about Roberts’s Rules, a collection of rules in plain English about how to design a database. • We’ll be careful to fully understand Roberts’s Rules. • Then we’ll talk about the basic normal forms: 1NF, 2NF, 3NF, BCNF and 4NF. • We’ll take time to understand the normal forms: what does each actually do? • Finally, we’ll look at the correspondence of the normal forms with Roberts’s Rules. • You will finish this exploration by additional exploration that you will do in your homework.

  15. Roberts’s Rules Roberts’s Rules are a set of plain English rules that, if followed during database design, result in a highly normalized database design. We will explore the relationship of Roberts’s Rules to normalization, and vice versa.

  16. Roberts’s Rules

  17. Rule 1 Each relation describes exactly one entity type. A relation models a distinct entity type, and each tuple of the relation models an instance of that entity. The relation models an entity by storing its attributes. The attributes that identify it are called candidate keys; the other attributes are non-key.


  19. Rule 2 Each fact is represented only once in the database. A tuple (aka row) is a collection of facts about an entity instance, one fact per column. Each fact can appear only once, in one row of one table.

  20. Duplicate Representation?

  21. Rule 3 Each tuple can reside in only one relation. A relation is a model of an entity type, not a station on a factory assembly line. Instead of moving a tuple from relation to relation, add an attribute that characterizes status.

  22. Rule 3 Example • As a person is being interviewed and hired, they change status: • Resume received • Resume being evaluated • Selected for interview • Selected for hire • Hired • As status changes, we could more the person’s row from one table to another. Should we?

  23. Rule 4 If the cardinality of an attribute is greater than one, then database design must be insensitive to cardinality. It’s easy—and very risky—to presume that the cardinality of various entity types and subtypes will remain the same.

  24. Rule 4 Examples • Company car • College degree • Telephone number • Home address • Business address • Email address

  25. Example of Roberts’s Rules EMP ( EMPNO, ENAME, DEPTNO, DNAME) DEPT (DEPTNO, DNAME, DLOC) This relation violates the following Roberts’s rules : • Rule 1. The EMP table describes employee as well as department • Rule 2. In the EMP table, if we have the same DEPTNO in multiple rows, DNAME will be represented multiple times.

  26. Another Example EMP (ENAME, DEGREE1, DEGREE2, DEGREE3) This schema violates the following Roberts’s rule : Rule 4. The design assumes every employee has a maximum of 3 degrees. If an employee has 4 degrees, then the database needs to be restructured by adding DEGREE4 in the EMP table. Rule 4 deals with an aspect of data independence. It can be stated informally as: "Grow down, not across"

  27. A Question Are Rule 1 and Rule 2 equivalent? They are equivalent if the set of relations that satisfy Rule 1 is the same as the set of relations that satisfies Rule 2. This is a homework problem.

  28. Normalization Preliminaries

  29. Normalization • A set of formal rules that are intended to be a definition of a properly-structured database • A normal form generally deals with and removes certain anomalous behavior from the use of a relation that is normalized.

  30. Examples of Anomalies • Insert anomalies • If we want to enter information about a new entity in the database we need to enter information about some other entity first • Delete anomalies • In order to delete information about an entity we must delete information about another entity • Update anomalies • In order to change the value of a single fact we may have to change many stored values in the database

  31. Basic Concepts • Entity Type: a class of an object that we record information about. Aka relation, table • Attribute: a characteristic of an entity. Aka column. • Entity Instance: a single occurrence of an entity type. Aka tuple, row

  32. Candidate Keys  Candidate key: a set of attributes Ai, Aj,…Ak that is a candidate key has two (time-invariant) properties: • Uniqueness – no two tuples have the same value for the candidate key • 2. Minimality – if any Ai is discarded from the candidate key, then the uniqueness property is lost. It is the smallest set of attributes that identifies a row. How many candidate keys can a table have?

  33. Primary Key One of the candidate keys is selected to be the primary identifier of rows. It is called the primary key. The selection is usually made based on the usefulness of the attribute that is the primary key.

  34. Functional Dependence • R.X→R.Y    or    R.X   FD    R.Y • Given a relation R, attribute Y of R is functionally dependent on attribute X of R iff each X-value in R has associated with it precisely one Y-value in R (at any one time) • In other words, for each value of X in table R, there is one and only one value of Y. A given X value must always occur with the same Y value.

  35. Functional Dependence Examples Does X→Y? Does Y→X?

  36. Anomalies Update anomalies: If one copy of repeated data is updated, inconsistency is created unless all copies are similarly updated. Insert anomalies: It may not be possible to store some information unless some other information is stored as well. Delete anomalies: It may not be possible to delete some information without losing some other information as well.

  37. Full Functional Dependence Y is fully functionally dependent on X iff X→Y and no subset of X determines Y. That is, X is the smallest collection of columns that determines Y.

  38. “Aboutness” FD is about “aboutness” If A is FD on X, then A is “about” X Suppose X is employee ID, EID; then EID determines salary, SAL But SAL is “about” the employee identified by EID

  39. Normalization

  40. First Normal Form

  41. First Normal Form A relation is said to be in first normal form iff every attribute of every tuple is atomic.

  42. 1NF Example Question: Is this relation in 1NF? Question: Does this relation show any anomalies?

  43. What’s not allowed by 1NF? 1NF doesn’t allow a relation to contain • Lists • Other relations • Multiple values

  44. Second Normal Form

  45. Second Normal Form A relation is said to be in second normal form iff it is in first normal form and every attribute is fully functionally dependent on the primary key.

  46. 2NF Example Does this relation follow Roberts’s Rules? Do you see any anomalies? SID City Status SNAME

  47. 2NF and RR What is the relationship between 2NF and Roberts’s Rules? If Rule 1 is met, is the relation in 2NF? What about Rule 2?

  48. What does 2NF not permit? 2NF doesn’t allow a relation to have information about more than one entity type

  49. Third Normal Form

  50. Third Normal Form A relation is said to be in third normal form iff it is in second normal form and there are no transitive dependencies.