1. COP 3540 - Introduction to Database Structures
DBMS Design, ER Model

2. Database design
The database design process can be divided into six steps.
Requirement Analysis
Conceptual Database Design: ER model
Logical Database Design: logical schema
Schema refinement: Normalization
Physical Database Design
Application and Security Design

3. Database design

4. Table / Relation / File

5. Overview of Database design
Conceptual design: (ER Model is used at this stage.)
What are the entities and relationships in the enterprise?
What information about these entities and relationships should we store in the database?
What are the integrity constraints or business rules that hold?
A database ‘schema’ in the ER Model can be represented pictorially (ER diagrams).
Can map an ER diagram into a relational schema.

6. Data Model
A data model is a collection of conceptual tools for describing data, data relationships, data semantics, and consistency constraints.
The entity–relationship (E-R) model is a high-level data model. It is based on a perception of a real world that consists of a collection of basic objects, called entities, and of relationships among these objects.
The relational model is a lower-level model. It uses a collection of tables to represent both data and the relationships among those data.

7. ER Model
The entity-relationship (E-R) data model perceives the real world as consisting of basic objects, called entities, and relationships among these objects. The E-R data model is one of several semantic data models; the semantic aspect of the model lies in its representation of the meaning of the data.

8. ER Model The E-R data model employs three basic notions: Entity Sets
Attributes, and
Relationship Sets.

9. Entity
An entity is a “thing” or “object” in the real world that is distinguishable from all other objects. For example, each person in an enterprise is an entity.
An entity has a set of properties, and the values for some set of properties may uniquely identify an entity. For instance, a person may have a person-id property whose value uniquely identifies that person.
An entity set is a set of entities of the same type that share the same properties, or attributes.

10. Entity An entity is represented by a set of attributes.
Attributes are descriptive properties possessed by each member of an entity set. For example, possible attributes of the customer entity set are customer-id, customer-name, customer-street, and customer-city.
Each entity has a value for each of its attributes.
For each attribute, there is a set of permitted values, called the domain, or value set, of that attribute.

11. Entity
Example of entity sets
Customer

12. Entity
Example of entity sets
Employees

13. Attribute
An attribute of an entity set is a function that maps from the entity set into a domain.
Since an entity set may have several attributes, each entity can be described by a set of (attribute, data value) pairs, one pair for each attribute of the entity set.

14. Attribute
An attribute can be characterized by the following attribute types:
Simple or composite attributes
Single-valued or multivalued attributes
Derived attribute
An attribute takes a null value when an entity does not have a value for it. The null value may indicate “not applicable”—that is, that the value does not exist for the entity. For example, one may have no middle name. Null can also designate that an attribute value is unknown. An unknown value may be either missing (the value does exist, but we do not have that information) or not known (we do not know whether or not the value actually exists).

15. Example

17. Attribute
Example of composite attributes

18. Composite Attribute

19. Composite Attribute

21. Composite Attribute

22. Composite Attribute

23. Keys
A superkey is a set of one or more attributes that, taken collectively, allow us to identify uniquely an entity in the entity set.
A candidate key is a minimal set of attributes whose values uniquely identify an entity in the set.
A primary key denotes a candidate key that is chosen by the database designer as the principal means of identifying tuples within a relation.

24. Examples

27. Relationship Sets Example of relationship
A relationship is an association among several entities.
Example of relationship

28. Relationship Sets
Relationships are described in terms of degree, connectivity, and existence.

29. Degree of a Relationship
The degree of a relationship is the number of entity types that participate in that relationship.
Binary relationship: a relationship between two types of entities.
Unary relationship: a relationship with another occurrence within the same entity.
Ternary relationship: a relationship directly involving three entity types.

30. Degree of a Relationship
Example a ternary relationship

31. Degree of a Relationship
Example a unary relationship

32. 1) Mapping Cardinalities 2) Participation Constraints

33. Mapping Cardinalities
Mapping Cardinalities: For a binary relationship set R between entity set A and B, the mapping cardinality must be one of the following:
One to One
One to Many
Many to One
Many to Many

34. Key Constraints
Consider Works_In: An employee can work in many departments; a dept can have many employees.

35. Key Constraints
In contrast, each dept has at most one (i.e., arrow line) manager, according to the key constraint on Manages.
dname
budget
did
since
lot
name
ssn
Manages
Employees
Departments

36. Mapping Cardinalities
Each department has at most one (i.e., arrow line) manager.

37. Mapping Cardinalities
Many to Many

38. Mapping Cardinalities
One to Many

39. Mapping Cardinalities
Many to One

40. Mapping Cardinalities
One to One

41. Mapping Cardinalities
Each employee works in at most one (i.e., arrow line) department and at a single location.

42. 1) Mapping Cardinalities 2) Participation Constraints

43. Participation Constraints
Total participate: The participation of an entity set E in a relationship set R is said to be total if every entity in E participates in at least one (i.e., thick line) relationship in R.
Partial Participate: If only some entities in E participate in relationships in R. The participation of entity set E in relationship R is said to be partial participation.

44. Participation Constraints
Participation constraints specifies whether the existence of an entity set depends on its being related to another entity.

45. Existence Dependency
If the existence of an entity x depends on the existence of another entity y, then x is said to be existence dependent on y.
Weak Entity type: The entity types that do not have key attributes of their own are called weak entity.
A weak entity type always has a total participation constraint with respect to its identifying relationship because a weak entity cannot be identified without an owner entity
Partial Key
Identifying relationship

46. Existence Dependency
A weak entity can be identified uniquely only by considering the primary key of another (owner) entity.
Owner entity set and weak entity set must participate in a one-to-many relationship set (one owner, many weak entities).
Weak entity set must have total participation in this identifying relationship set.
name
cost
ssn
pname
lot
age
Employees
Policy
Dependents

47. ISA (‘is a’) Hierarchies
As in C++, or other PLs, attributes are inherited.
If we declare A ISA B, every A entity is also considered to be a B entity.
Overlap constraints: Can Joe be an Hourly_Emps as well as a Contract_Emps entity? (Allowed/disallowed)
Covering constraints: Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no)
Reasons for using ISA:
To add descriptive attributes specific to a subclass.
To identify entities that participate in a relationship

48. Generalization / Specialization

49. Aggregation
Used when we have to model a relationship involving (entity sets and) a relationship set.
Aggregation allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships
Aggregation vs. ternary relationship:
Monitors is a distinct relationship, with a descriptive attribute.
Also, can say that each sponsorship is monitored by at most one employee.

50. Aggregation

51. 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? Aggregation?
Constraints in the ER Model:
A lot of data semantics can (and should) be captured.
But some constraints cannot be captured in ER diagrams.

52. Entity vs. Attribute
Should address be an attribute of Employees or an entity (connected to Employees by a relationship)?
Depends upon the use we want to make of address information, and the semantics of the data:
If we have several addresses per employee, address must be an entity (since attributes cannot be set-valued).
If the structure (city, street, etc.) is important, e.g., we want to retrieve employees in a given city, address must be modeled as an entity (since attribute values are atomic).

53. Example 1
Works_In2 does not allow an employee to work in a department for two or more periods.

54. Example 2
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.

55. Example 3
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?
Redundancy: dbudget stored for each dept managed by manager.
Misleading: Suggests dbudget associated with department-mgr combination.

56. Example 4

57. Example 5 name ssn lot since dname Employees did budget Manages2
Departments
ISA
Managers
dbudget

58. Example 6 (Binary vs. Ternary Relationships)
If each policy is owned by just 1 employee, and each dependent is tied to the covering policy, first diagram is inaccurate.

59. Example 7 (Binary vs. Ternary Relationships)
What are the additional constraints in this diagram?

60. Example 8

61. Summary of Conceptual Design
Conceptual design follows requirements analysis,
Yields a high-level description of data to be stored
ER model popular for conceptual design
Constructs are expressive, close to the way people think about their applications.
Basic constructs: entities, relationships, and attributes (of entities and relationships).
Some additional constructs: weak entities, ISA hierarchies, and aggregation.
Note: There are many variations on ER model.

62. Summary of ER (Contd.)
Several kinds of integrity constraints can be expressed in the ER model: key constraints, participation constraints, and overlap/covering constraints for ISA hierarchies. Some foreign key constraints are also implicit in the definition of a relationship set.
Some constraints (notably, functional dependencies) cannot be expressed in the ER model.
Constraints play an important role in determining the best database design for an enterprise.

63. Summary of ER
ER design is subjective. There are often many ways to model a given scenario! Analyzing alternatives can be tricky, especially for a large enterprise. Common choices include:
Entity vs. attribute, entity vs. relationship, binary or n-ary relationship, whether or not to use ISA hierarchies, and whether or not to use aggregation.
Ensuring good database design: resulting relational schema should be analyzed and refined further. FD information and normalization techniques are especially useful.