The Entity-Relationship Model

1. The Entity-Relationship Model 2

2. Database Design Process • Requirement collection and analysis • DB requirements and functional requirements • Conceptual DB design using a high-level model • Easier to understand and communicate with others • Logical DB design (data model mapping) • Conceptual schema is transformed from a high-level data model into implementation data model • Physical DB design • Internal data structures and file organizations for DB are specified

3. 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). • An ER diagram can be mapped into a relational schema.

4. The Relational Model Relational Model [Properties] • Each relation (or table) in a database has a unique name • An entry at the intersection of each row and column is atomic (or single-valued); there can be no multi-valued attributes in a relation • Each row is unique; no two rows in a relation are identical • Each attribute (or column) within a table has a unique name

5. The Relational Model Properties Cont’d • The sequence of columns (left to right) is insignificant; the columns of a relation can be interchanged without changing the meaning or use of the relation • The sequence of rows (top to bottom) is insignificant; rows of a relation may be interchanged or stored in any sequence

6. The Relational Model... • The relational model of data has three major components: • Relational database objects • allows to define data structures • Relational operators • allows manipulation of stored data • Relational integrity constraints • allows to defines business rules and ensure data integrity

7. Marketing Accounting Sales Advertising Accounts Receivable Accounts Payable The Relational Objects • Location Most RDBMS can have multiple locations, all managed by the same database engine Corporate Database Accounting Marketing Purchasing

8. Database Server Multi-user The Relational Objects • Location Client Applications

9. The Relational Objects... Database Server • Database • A set of SQL objects Update Trigger BEGIN ... Client Application Table T Insert Trigger UPDATE T SET INSERT INTO T DELETE FROM T CALL STPROG BEGIN ... Delete Trigger Stored Procedure Table A BEGIN ... BEGIN ... Table B

10. The Relational Objects... • Database • A collection of tables and associated indexes Index Table Table Employee Product Table Table Files Department Customer

11. The Relational Objects... • Relation • A named, two dimensional table of data • Database • A collection of databases, tables and related objects organised in a structured fashion. • Several database vendors use schema interchangeably with database

12. Relational Objects... Data is presented to the user as tables: • Tables are comprised of rowsand a fixed number of named columns. Table Column 1 Column 2 Column 3 Column 4 Row Row Row

13. Relational Objects... Data is presented to the user as tables: • Columns are attributes describing an entity. Each column must have an unique name and a data type. Employee Name Designation Department Row Row Row Structure of a relation (e.g. Employee) Employee(Name, Designation, Department)

14. Relational Objects... Data is presented to the user as tables: • Rows are records that present information about a particular entity occurrence Employee Name Designation Department Row De Silva Manager Personnel Row Perera Secretary Personnel Dias Manager Sales Row

15. Relational model terminology • Row is called a ‘tuple’ • Column header is called an ‘attribute’ • Table is called a ‘relation’ • The data type describing the type of values that can appear in each column is called a ‘domain’ • Eg:- • Names : the set of names of persons • Employee_ages : value between 15 & 80 years old The above is called ‘logical definitions of domains’. A data type or format can also be specified for each domain. Eg: The employee age is an integer between 15 and 80

16. Characteristics of relations • Ordering of tuples • Tuples in a realtion don’t have any particular order. How ever in a file they may be physically ordered based on a criteria, this is not there in relational model • Ordering of values within tuple • Ordering of values within a tuple are unnecessary, hence a tuple can be considered as a ‘set’. • But when relation is implemented as a file attributes may be physically ordered • Values in a tuple are atomic

17. Relational constraints • Domain constraints • specifies that the value of each attribute ‘A’ must be an atomic value. And from the specified domain • Key constraints • There is a sub set of attributes of a relational schema with the property that no two tuples should have the same combination of values for the attributes. • Any such subset of attributes is called a ‘superkey’ • A ‘superkey’ can have redundant attributes. A key is a minimul superkey • If a realtion has more than one key, they are called candidate keys • One of them is chosen as the primary key

18. Relational Objects Keys • Primary Key:An attribute (or combination of attributes) that uniquely identifies each row in a relation. Employee(Emp_No, Emp_Name, Department) • Composite Key: A primary key that consists of more than one attribute Salary(Emp_No, Eff_Date, Amount)

19. Employee E-NoE-Name D-No 179 Silva 7 857 Perera 4 342 Dias 7 Salary E-No Eff-Date Amt 179 1/1/98 8000 857 3/7/94 9000 179 1/6/97 7000 342 28/1/97 7500 Primary Key Relational Objects Data is presented to the user as tables: • Each table has a primary key. The primary key is a column or combination of columns that uniquely identify each row of the table. Primary Key

20. Salary E-NoEff-Date Amt 179 1/1/98 8000 857 3/7/94 9000 179 1/6/97 7000 342 28/1/97 7500 Relational Objects Data is presented to the user as tables: • The cardinality of a table refers to the number of rows in the table. The degreeof a table refers to the number of columns. Salary Table Degree = 3 Cardinality = 4

21. Entity integrity, referential integrity/foreign keys • Entity integrity constraint specifies that no primary key can be null • The referential integrity constraint is specified between two relations and is used to maintain the consistency among tuples of the two realtions • Informally what this means is that a tuple in one relation that refers to another relation must refer to an existing tuple. • To define referential integrity we use the concept of foreign keys.

22. Relational Objects Relationship • Foreign Key:An attribute in a relation of a database that serves as the primary key of another relation in the same database Employee(Emp_No, Emp_Name, Department) Department(Dept_No, Dept_Name, M_No) === works for ==>

23. Employee E-No E-Name D-No 179 Silva 7 857 Perera 4 342 Dias 7 Relational Objects Data is presented to the user as tables: • Aforeign keyis a set of columns in one table that serve as the primary key in another table Department D-No D-Name M-No 4 Finance 857 7 Sales 179 Primary Key Primary Key Primary Key Foreign Key Recursive foreign key: A foreign key in a relation that references the primary key values of that same relation

24. Employee Foreign Key E-No E-Name D-No 179 Silva 7 857 Perera 4 342 Dias 7 Salary E-NoEff-Date Amt 179 1/1/98 8000 857 3/7/94 9000 179 1/6/97 7000 342 28/1/97 7500 Foreign Key Primary Key Department Relational Objects... D-No D-Name M-No 4 Finance 857 7 Sales 179 Primary Key Primary Key Primary Key Primary Key Foreign Key Rows in one or more tables are associated with each other solely through data values in columns (no pointers).

25. Relational Objects • Index • An ordered set of pointers to the data in the table Employee E-Name Pointer De Silva Dias Perera Silva E-No E-Name D-No 179 Silva 7 857 Perera 4 342 Dias 7 719 De Silva 5

26. E-Name Pointer Alwis Bandara Costa De Silva Dias Opatha Peiris Perera Silva Vaas Wickrama Zoysa Index: Employee Name Employee E-No E-Name D-No 179 Silva 7 857 Perera 4 342 Dias 7 719 De Silva 5 587 Alwis 4 432 Costa 6 197 Zoysa 2 875 Peiris 4 324 Vaas 7 917 Bandara 3 785 Opatha 2 234 Wickrama 1

27. E-Name Pointer Alwis Bandara Costa De Silva Dias Opatha Peiris Perera Silva Vaas Wickrama Zoysa Search: Employee Dias • Index Improves performance. Access to data is faster

28. Ensures uniqueness. A table with unique fields in the index cannot have two rows with the same values in the column or columns that form the index key. Search: Employee Dias • Index Opatha Costa Silva Bandara Dias Perera Wickrama

29. Search: Employee Dias . De Silva . Perera . . Bandara . . . Opatha . . . Vaas . . . Wickrama . Zoysa . . Alwis . . . Costa . . . Dias . . . Peiris . . . Silva . .

30. STORE Store Name | City INVENTORY Store Name | Part No | Quantity ORDERS Store Name | Part No | Vendor No | Order No | Quantity PART Part No | Description VENDOR Vendor No | Vendor Name Relational Database STORE Store 1 | Colombo Store 2 | Kandy INVENTORY Store 1 | P1 | 50 Store 1 | P3 | 20 Store 2 | P2 | 100 Store 2 | P1 | 30 ORDERS Store 1 | P3 | 3428 | 0052 | 10 Store 2 | P2 | 3428 | 0098 | 7 Store 2 | P3 | 3428 | 0098 | 15 Store 2 | P4 | 5726 | 0099 | 1 PART P1 | Printer P2 | Diskette P3 | Disk Drive P4 | Modem VENDOR 3428 | East West 5726 | DMS

31. name ssn lot Employees ER Model Basics • Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. • Entity Set: A collection of similar entities. E.g., all employees. • All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!) • Each entity set has a key. • Each attribute has a domain.

32. name ssn lot Employees ER Model Basics • Key and key attributes: • Key: a unique value for an entity • Key attributes: a group of one or more attributes that uniquely identify an entity in the entity set • Super key, candidate key, and primary key • Super key: a set of attributes that allows to identify and entity uniquely in the entity set • Candidate key: minimal super key • There can be many candidate keys • Primary key: a candidate key chosen by the designer • Denoted by underlining in ER attributes

33. name ER Model Basics (Contd.) ssn lot Employees since name dname • Relationship: Association among two or more entities. e.g., Jack works in Pharmacy department. • Relationship Set: Collection of similar relationships. • An n-ary relationship set R relates n entity sets E1 ... En; each relationship in R involves entities e1 in E1, ..., en in En • Same entity set could participate in different relationship sets, or in different “roles” in same set. super-visor subor-dinate ssn budget lot did Reports_To Works_In Employees Departments

34. since name dname ssn lot Employees Manages Key Constraints did budget • Consider Works_In: An employee can work in many departments; a dept can have many employees. • In contrast, each dept has at most one manager, according to the key constrainton Manages. Departments 1-to-1 1-to Many Many-to-1 Many-to-Many

35. Department Example ER major offers faculty • An ER diagram represents several assertions about the real world. What are they? • When attributes are added, more assertions are made. • How can we ensure they are correct? • A DB is judged correct if it captures ER diagram correctly. Courses teaches Professor advisor enrollment Students

36. Participation Constraints • Does every department have a manager? • If so, this is a participation constraint: the participation of Departments in Manages is said to be total (vs. partial). • Every Departments entity must appear in an instance of the Manages relationship. since since name name dname dname ssn did did budget budget lot Departments Employees Manages Works_In since

37. Weak Entities • 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 pname age ssn lot Policy Dependents Employees

38. name ssn lot ISA (`is a’) Hierarchies Employees hours_worked • 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. hourly_wages ISA • Overlap constraints: Can Joe be an Hourly_Emps as well as a Contract_Emps entity? (default: disallowed; A overlaps B) • Covering constraints: Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (default: no; A AND B COVER C) • Reasons for using ISA: • To add descriptive attributesspecific to a subclass. • To identify entities that participate in a relationship. contractid Contract_Emps Hourly_Emps

39. Employees name ssn lot Aggregation Monitors until • Used when we have to model a relationship involving (entitity sets and) a relationship set. • Aggregation allows us to treat a relationship set as an entity set for purposes of participation in (other) relationships. since started_on dname pid pbudget did budget Sponsors Departments Projects • 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.

40. 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.

41. Entity vs. Attribute • Should addressbe 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).

42. name dname ssn lot did Employees dname did budget Duration to from Entity vs. Attribute (Contd.) to from budget • Works_In4 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. Departments Works_In4 name ssn lot Works_In4 Departments Employees

43. Entity vs. Relationship since dbudget name dname • 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. ssn lot did budget Departments Employees Manages2 name ssn lot dname since did Employees budget Departments Manages2 ISA This fixes the problem! Managers dbudget

44. name ssn lot Employees Policies policyid cost name ssn lot Employees Beneficiary Policies policyid cost Binary vs. Ternary Relationships pname age Dependents Covers • If each policy is owned by just 1 employee, and each dependent is tied to the covering policy, first diagram is inaccurate. • What are the additional constraints in the 2nd diagram? Bad design pname age Dependents Purchaser Better design

45. Binary vs. Ternary Relationships (Contd.) • Previous example illustrated a case when two binary relationships were better than one ternary relationship. • An example in the other direction: a ternary relation Contracts relates entity sets Parts, Departments and Suppliers, and has descriptive 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. • How do we record qty?

46. 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.

47. Summary of ER (Contd.) • Several kinds of integrity constraints can be expressed in the ER model: key constraints, participationconstraints, 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.

48. Summary of ER (Contd.) • 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.