The Relational Model Mapping the ER Model to a Database Implementation

1. The Relational ModelMapping the ER Model to a Database Implementation

2. The Relational Model • Mathematically based • Can develop theoretical design improvements & enhancements that result in applications to many different applications • Can use exact mathematical notation • Basic structure is simple, easy to understand • Separates logical from physical level • Operations do not require user to know storage structures used • Data operations easy to express, using a few powerful commands

3. Tables • Relations are represented logically as tables • Tables are related to one another • Table holds information about entities • Table rows correspond to individual records • Table columns correspond to attributes • A column contains values from one domain • Domains consist of atomic (single) values

4. Properties of Tables • Each cell contains at most one value • It is a single piece of data • Each column has a distinct name • This is the name of the attribute it represents • Values in a column all come from the same domain • Each tuple is distinct – no duplicate tuples

5. Sample: ER to Relational Model Enrolls Taught by STUDENT CLASS FACULTY • Student (stuId, lastName, firstName, major, credits) • Class (classNum, facId, schedule, room) • Faculty (facId, name, department, rank) • Enroll(stuId,classNum,grade)

6. Representing Relational Database Schemas • Can have any number of relation schemas • Example: University database schema Student (stuId, lastName, firstName, major, credits) Class (classNumber, facId, schedule, room) Faculty (facId, name, department, rank) Enroll(stuId,classNumber,grade) • This could also be represented by the relationships screen of the DBMS.

7. Properties of Relations (Tables) • Degree: the number of attributes • Binary, ternary, n-ary • Connectivity: • 1:1, 1:N, M:N • Cardinality: the number of tuples • Changes as tuples are added or deleted • Keys • Constraints

8. Keys • Relations never have duplicate tuples (rows) • You can always tell tuples apart / there is always a key • Superkey: set of attributes that uniquely identifies tuples • Candidate key: minimal superkey • No proper subset of itself is also a superkey • Primary key (PK): candidate key chosen to uniquely identify tuples • You cannot verify a key by looking at an instance – why? • Foreign key (FK) is an attribute or combination of attributes of a relation that is the PK of another relation

9. Selecting the Primary Key • An ideal primary key is short, numeric, and seldom changing • If there is more than one candidate key, each should be carefully evaluated • If the entity has no identifier, some attribute must be selected as the PK • In some situations, a surrogate key may be defined

10. Surrogate Keys • These are unique, DBMS-supplied identifiers used as the PK of a relation • The values of a surrogate key have no meaning to users and are normally hidden on forms and reports • The DBMS does not allow the value of a surrogate key to be changed • Disadvantages: • FK’s based on surrogate keys have no meaning to users • When data shared among different databases contain the same ID, merging those tables might yield unexpected results

11. Constraints • Integrity constraints – to ensure “correctness and internal consistency” • Rules or restrictions that apply to all instances of the database • Enforcing them ensures only legal states of the database are created • Types of integrity constraints • Domain constraint - limits set of values for an attribute • Entity integrity - no part of a PK can be null • Referential integrity - each FK value must match the primary key value of some tuple in its related relation, or be null • General constraints are the business rules • These may be expressed as table constraints or assertions • Participation constraintsreflectthe extent of entities’ involvement in given relationships

12. The Database Implementation Process • This is the step in the database design process that follows the conceptual design/ERD • Create tables and columns from entities and attributes • Select primary keys • Represent relationships • Specify constraints • Performance tuning

13. Mapping the ERD to a Relational Model • Entities • Issues with composite & multi-valued attributes • Issues with weak entities • Relationships • 1:1, 1:N and M:N • Participation constraints

14. Mapping an Entity to a Table • Each entity maps onto a table • Its non-composite, single-valued attributes comprise the table’s column headings • For composite attributes, there are 3 possible ways: • Make the composite into a single attribute • Create several individual attributes to replace the composite • Create a new entity • For multi-valued attributes, we create a new table • The PK of this new table is the composite of the original attribute coupled with the PK of the original table • This new table is created as a weak entity • Example: multiple email accounts

15. Mapping a Relationship • Binary Relationships: • 1:M • PK of 1-side becomes a FK of the M-side table • 1:1 • First, make sure they are not the same entity. If not, use either PK as the FK in the other table • M:M • Create a relationship table (bridge, composite) with a composite PK consisting of the PK’s of the related entities, along with any relationship attributes • Ternary or higher degree relationships: construct relationship table of keys, along with any relationship attributes • With all relationships, we must preserve referential integrity, participation & cardinality constraints

16. 1:1 Relationship Has EMPLOYEE 1 AUTO 1

17. 1:N Relationship Teaches FACULTY N CLASS 1

18. M:N Relationship Enrolls STUDENT CLASS • Student (stuId, lastName, firstName, major, credits) • Enroll(stuId,classNum,grade) • Class (classNum, facId, schedule, room)

19. Weak entities • Weak entities become tables by adding the PK of the parent (strong) entity • What is the primary key in such a table? • What about the total participation (and existence dependence) constraint? • What do we need to ensure? • How do we do this?

20. RDBMS Rules Regarding FK’s • Choices made when relationships are established between tables • Cascade deletes • If parent instance is deleted, so are all its children • Restrict deletes • Cannot delete a parent instance if it has a child • Set to NULL • If parent instance is deleted, set the FK of the child to NULL • Cascade updates • If parent instance gets new PK, change the FK of all its children

21. Total participation • Referential integrityactions need to be specified to ensure that • When the parent entity instance is deleted, the weak entity instance is deleted as well • Each new weak entity instance must have a parent instance with which to connect • Other participation situations also require careful attention. • They must be reasoned out.

22. Weak Entity Example

23. Enforcing Minimum Cardinality • If the minimum cardinality on the child is 1, at least one child row must be connected to the parent • A required parent can be specified by making the foreign key value NOT NULL • A required child can be represented by creating update and delete referential integrity actions on the child and insert referential integrity actions on the parent • Such referential integrity actions must be declared during database design and trigger codes must be written during implementation

24. Subtype Relationship

25. Entity Supertypes and Subtypes • Generalization hierarchy • Depicts a relationship between a higher-level supertype entity and a lower-level subtype entity • Supertype entity • Contains shared attributes • Subtype entity • Contains unique attributes

26. Nulls Created with No Supertypes/Subtypes

27. A Generalization Hierarchy

28. Disjoint Subtypes • Also known as non-overlapping subtypes • Subtypes that contain a subset of the supertype entity set • Each entity instance (row) of the supertype can appear in only one of the disjoint subtypes • Supertype and its subtype(s) maintain a 1:1 relationship • So, how is it implemented in an rdbms?

29. EMPLOYEE/PILOT Supertype/Subtype Relationship

30. A Generalization Hierarchy with Overlapping Subtypes

31. Recursive Relationships • A recursive relationship is a relationship among entities of the same class • For 1:1 and 1:N recursive relationships, add a foreign key to the relation that represents the entity

32. 1:1 Recursive Relationships

33. 1:N Recursive Relationships

34. Some issues with conceptual design using the ER model • Design choices: • Should a concept be modeled as an entity or an attribute? • Should a concept be modeled as an entity or a relationship? • Identifying relationships: Binary or ternary? • When to allow NULL values? • How should privacy concerns/sensitive data collection be handled? • Constraints in the ER Model: • A lot of data semantics can (and should) be captured. • But some constraints cannot be captured in ER diagrams.

35. Entity vs. Attribute • Should addressbe an attribute of EMPLOYEE or an entity (connected to EMPLOYEE by a relationship)? • Depends upon the use we want to make of address data, and the semantics of the data: • If we have several addresses per employee, address must be an entity • If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address can be modeled as an entity or as several attributes

36. name dname eid lot did Employees dname did budget Duration to from Entity vs. Attribute example to from • Works_In does not allow an employee to work in a department for two or more periods. • Similar to the problem of wanting to record several addresses for an employee: We want to record several values of the descriptive attributes for each instance of this relationship. • Accomplished by introducing new entity set, Duration. budget Departments Works_In name eid lot Works_In Departments Employees

37. Entity vs. Relationship • First ER diagram OK if a manager gets a separate discretionary budget for each dept. • What if a manager gets a discretionary budget that covers all managed depts? since dbudget name dname eid lot did budget Department Employee Manages name eid lot dname since did Employee budget Could also be a supertype Department Manages Is a Manager dbudget

38. name eid lot Employee Policy policyid cost name eid lot Employee Beneficiary Policy policyid cost Binary vs. Ternary Relationships pname age What are the differences between these two diagrams? Covers Dependent Design 1 pname age Dependent Purchaser Design 2

39. Binary vs. Ternary Relationships • An example where a ternary relation is required • Contracts relates entity sets PART, DEPT and SUPPLIER, and has attribute qty. • No combination of binary relationships is an adequate substitute: • S “can-supply” P, D “needs” P, and D “deals-with” S does not imply that D has agreed to buy P from S. • And, furthermore, how would we record qty?

40. Null values • A null value is an attribute value that has not been supplied • Null values are ambiguous as they can mean • The value is unknown • The value is inappropriate • The value is known to be blank • Inappropriate nulls can be avoided by • Defining subtype or category entities • Forcing attribute values through the use of not null • Supplying initial values • Ignore nulls if the ambiguity is not a problem to the users

41. Surrogate Key Example