Design Concepts & ER Model

1. Design Concepts & ER Model Chapter 2 Instructor: Xin Zhang

2. Inside a Database • Tables • Relationship among tables • Operations (queries)

3. Overview of db design • Requirement analysis • Data to be stored • Applications to be built • Operations (most frequent) subject to performance requirement • Conceptual db design • Description of the data (including constraints) • By high level model such as ER • Logical db design • Choose DBMS to implement • Convert conceptual db design into database schema Beyond ER design • Schema refinement (normalization) • Physical db design • Analyze the workload • Refine db design to meet performance criteria (focus on Indexing) • Security design

4. Conceptual design • Issues to consider: (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 (i.e., attributes)? • What are the integrity constraintsor business rulesthat hold? • Solution: • A database `schema’ in the ER Model can be represented pictorially (ER diagrams). • Can map an ER diagram into a relational schema.

5. 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. • Each entity set has a key. • Each attribute has a domain. What’s the key? How many keys one object can have?

6. Key • A key is a minimal set of attributes whose values uniquely identify an entity in the set. • Candidate key. • Primary key.

7. Entity, Entity Set, Attribute, and Schema ID or SSN Name UserID Age GPA 999-38-4431 John Smith jsmith 21 3.68 999-28-3341 mjordan Miki Jordan 3.45 28 David Kim dkim 4.00 331-43-4567 25 Paul Lee 26 535-34-5678 plee 3.89

8. ER Model Basics (Contd.) since name dname • Relationship: Association among 2 or more entities. E.g., Sam works in the Accounting 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 E1, ..., en En • Same entity set could participate in different relationship sets, or in different “roles” in same set. ssn budget lot did Works_In Departments Employees Relationship Set

9. Entity vs. Entity Set Object --- Student John Smith (999-21-3415, jsmith@, John Smith, 18, 3.5) Students in ITCS3160 999-21-3415, jsmith@, John Smith, 18, 3.5 999-31-2356, jzhang@, Jie Zhang, 20, 3.0 999-32-1234, ajain@, Anil Jain, 21, 3.8

10. Example of Keys Primary key Candidate key 999-21-3415, jsmith@, John Smith, 18, 3.5 999-31-2356, jzhang@, Jie Zhang, 20, 3.0 999-32-1234, ajain@, Anil Jain, 21, 3.8

11. Relationship vs. Relationship Set John Smith (999-21-3415, jsmith@, John Smith, 18, 3.5) Relationship ITCS3160 (3160, ITCS, DBMS, J. Fan, 3, Kenn. 236)

12. Relationship vs. Relationship Set 999-21-3415, jsmith@, John Smith, 18, 3.5 Students 999-31-2356, jzhang@, Jie Zhang, 20, 3.0 999-32-1234, ajain@, Anil Jain, 21, 3.8 Relationship set(“Enrolled in”) 3160, ITCS, DBMS, J. Fan, 3, Kenn. 236 Courses 6157, ITCS, Visual DB, J. Fan, 3, Kenn. 236

13. Login Age GPA Name Credit Grade Students Courses Relationship vs. Relationship Set Name Id room Id Enrolled_In Descriptive attribute

14. Example 1 • Build an ER Diagram for the following information: • Students • Have an Id, Name, Login, Age, GPA • Courses • Have an Id, Name, Credit Hours • Students enroll in courses • Receive a grade

15. Login Age GPA Name Credit Grade Students Courses Example 1 Answer Name Id Id Enrolled_In

16. Example 2 • Build an ER Diagram for the following information: • Patients • Name, Address, Phone #, Age • Drugs • Name, Manufacturer , Expiration Date • Patients are prescribed drugs • Dosage, # Days

17. Addr Phone Age Manuf Exp Patients Example 2 Answer Name Name Drug Prescribed #days Dosage

18. Constraints • Key constraints • Participation constraints

19. Potential Relationship Types 1-to Many Many-to-1 1-to-1 Many-to-Many

20. Potential Relationship Types ? ? Students IN CS Dept • Mary studies in the CS Dept. • Tom studies in the CS Dept. • Jack studies in the CS Dept. • … • The CS Dept has lots of students. • No student in the CS Dept works in other else Dept at the same time.

21. Potential Relationship Types ? ? Students take Courses • Mary is taking the ITCS3160,ITCS2212. • Tom is taking the ITCS3160, ITCS2214. • Jack is taking the ITCS1102, ITCS2214. • … • 61 students are taking ITCS3160. • 120 students are taking ITCS2214.

22. Key Constraints why "work-in" is not "key constraint"? • Consider Works_In: An employee can work in many departments; a dept can have many employees. since name dname ssn budget lot did Works_In Departments Employees

23. Key Constraints • Consider Works_In: An employee can work in at most one department; a dept can have many employees. since name dname ssn budget lot did Works_In Departments Employees

24. since name dname ssn lot Manages Employees why "manage" should be "key constraint"? Key Constraints At most one!!! • In contrast, each dept has at most one manager, according to the key constrainton Manages. did budget Departments Key Constraint (time constraint)

25. since 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 did value in Departments table must appear in a row of the Manages table (with a non-null ssn value!) since since name name dname dname ssn did did budget budget lot Departments Employees Manages Total w/key constraint Partial Total Works_In Total

26. since What are the policies behind this ER model? since since name name dname dname ssn did did budget budget lot Departments Employees Manages Total w/key constraint Total Total Works_In Total

27. since name dname ssn lot Manages Employees since did budget Departments Any Difference? Works_In since since name name dname dname ssn did did budget budget lot Departments Employees Manages Total w/key constraint Partial Total Works_In Total

28. Weak Entities vs. Owner 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 (1 owner, many weak entities). • Weak entity set must have total participation in this identifying relationship set. name cost pname age ssn lot Primary Key for weak entity Policy Dependents Employees Identifying Relationship Weak Entity

29. dname did budget Duration to from Ternary Relationship name ssn lot Works_In3 Departments Employees Why? since name dname ssn budget lot did Works_In Departments Employees

30. name ssn lot ISA (`is a’) Hierarchies Employees hours_worked hourly_wages • As in C++, or other PLs, attributes are inherited. ISA • 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 attributesspecific to a subclass. • To identify entitities that participate in a relationship. contractid • If we declare A ISA B, every A entity is also considered to be a B entity. Contract_Emps Hourly_Emps

31. 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. • Monitors mapped to table like any other relationship set. Aggregation started_on dname pid pbudget did budget Sponsors Departments Projects

32. Real Database Design • Build an ER Diagram for the following information: • Walmart Stores • Store Id, Address, Phone # • Products • Product Id, Description, Price • Manufacturers • Name, Address, Phone # • Walmart Stores carry products • Amount in store • Manufacturers make products • Amount in factory/warehouses

33. 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? Always follow the requirements.

34. 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).

35. name dname ssn lot did Employees dname did budget Duration to from Entity vs. Attribute (Contd.) to from budget • Works_In2 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. Departments Works_In2 name ssn lot Works_In3 Departments Employees

36. name dname ssn lot did budget Departments Manages3 Employees since Mgr_Appts apptnum dbudget Entity vs. Relationship since dbudget name dname • First ER diagram OK if a manager gets a separate discretionary budget for each dept. • Redundancy of dbudget, which is stored for each dept managed by the manager. • Misleading: suggests dbudget tied to managed dept. • What if a manager gets a discretionary budget that covers all managed depts? ssn lot did budget Departments Employees Manages2

37. 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: • Key constraint on Policies would mean policy can only cover 1 dependent! Bad design pname age Dependents Purchaser Better design

38. Binary vs. Ternary Relationships (Contd.) • Previous example illustrated a case when binary relationships were better than one ternary relationship. • An example in the other direction: a ternary relation Contracts relates entity set Parts, Departments and Suppliers, and has descriptive attributes 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?

39. 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.

40. 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.

41. 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.

42. Homework Assignment • Problem 2.3 at the end of Chapter 2 • Pages 52 • Due Jan 18, 2007: send e-version • Format for homework: name, ID.