Database design
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The document discusses the entity-relationship (ER) model for conceptual database design. It describes the basic constructs of the ER model including entities, attributes, relationships, keys, and various modeling choices. The ER model is useful for capturing the semantics of an application domain and producing a conceptual schema before logical and physical design.
Database design
- 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. 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. Employees ssn name lot
- 5. 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 Employees ssn name lot
- 6. ER Model Basics (Contd.) • 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. lot dname budgetdid since name Works_In DepartmentsEmployees ssn Reports_To lot name Employees subor- dinate super- visor ssn
- 7. Key Constraints • 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 constraint on Manages. Many-to-Many1-to-1 1-to Many Many-to-1 dname budgetdid since lot name ssn ManagesEmployees Departments
- 8. Example ER • 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. Students Professor teaches Department faculty major offers Courses enrollment advisor
- 9. Exercises • Is double major allowed? • Can a student have more than 1 advisor? • Is joint appointment of faculty possible? • Can two profs share to teach the same course? • Can a professor teach more than one course? • Can a professor stay without affiliated with a department? Students Professor teaches Department faculty major offers Courses enrollment advisor
- 10. 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. lot name dname budgetdid since name dname budgetdid since Manages since DepartmentsEmployees ssn Works_In
- 11. 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. lot name agepname DependentsEmployees ssn Policy cost
- 12. ISA (`is a’) Hierarchies • 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 attributes specific to a subclass. – To identify entities that participate in a relationship. Contract_Emps name ssn Employees lot hourly_wages ISA Hourly_Emps contractid 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.
- 13. Aggregation • 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. 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. budgetdidpid started_on pbudget dname until DepartmentsProjects Sponsors Employees Monitors lot name ssn since
- 14. 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.
- 15. 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).
- 16. Entity vs. Attribute (Contd.) • 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. name Employees ssn lot Works_In4 from to dname budgetdid Departments dname budgetdid name Departments ssn lot Employees Works_In4 Durationfrom to
- 17. 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? – Redundancy: dbudget stored for each dept managed by manager. – Misleading: Suggests dbudget associated with department-mgr combination. Manages2 name dname budgetdid Employees Departments ssn lot dbudgetsince dname budgetdid DepartmentsManages2 Employees name ssn lot since Managers dbudget ISA This fixes the problem!
- 18. 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. • What are the additional constraints in the 2nd diagram? agepname DependentsCovers name Employees ssn lot Policies policyid cost Beneficiary agepname Dependents policyid cost Policies Purchaser name Employees ssn lot Bad design Better design
- 19. 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?
- 20. 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.
- 21. 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.
- 22. 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.
- 23. Exercise • What can you say about policy of the bank from the ER diagram? • What can you say about the policy of the company? balanceacct# AccountCustAcct name Customer ssn addr deptid budget Dept Manages name Employees ssn lot
- 24. Design Exercise • Design a DB using ER, and sketch the resulting diagram. State any important assumptions you made in reaching the design. Show explicitly whether relationships are 1-1, 1-M, or N-M. – UMW registrar’s office: It maintains data about each class, including the instructor, students, enrollment, time and place of the class meetings. For each student- class pair, a grade is recorded. – Hospital: It maintains all patients visited, including age and address. It also keeps track of the information about billing, visits, data, reason for visit, and treatment.
Editor's Notes
- #2: The important issue of how to map from ER diagrams to relational tables is deferred until the relational model and the integrity constraints it supports have been introduced. ER to relational mapping, together with a discussion of the related SQL commands, is discussed in Chapter 3.
- #5: The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This covers Lectures 1 and 2 (of 6) in Module (5). Module (1): Introduction (DBMS, Relational Model) Module (2): Storage and File Organizations (Disks, Buffering, Indexes) Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security) Module (4): Relational Implementation (Query Evaluation, Optimization) Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning) Module (6): Transaction Processing (Concurrency Control, Recovery) Module (7): Advanced Topics
- #6: The slides for this text are organized into several modules. Each lecture contains about enough material for a 1.25 hour class period. (The time estimate is very approximate--it will vary with the instructor, and lectures also differ in length; so use this as a rough guideline.) This covers Lectures 1 and 2 (of 6) in Module (5). Module (1): Introduction (DBMS, Relational Model) Module (2): Storage and File Organizations (Disks, Buffering, Indexes) Module (3): Database Concepts (Relational Queries, DDL/ICs, Views and Security) Module (4): Relational Implementation (Query Evaluation, Optimization) Module (5): Database Design (ER Model, Normalization, Physical Design, Tuning) Module (6): Transaction Processing (Concurrency Control, Recovery) Module (7): Advanced Topics