The Entity-Relationship Model Chapter 2 Database Management Systems

The Entity-Relationship Model Chapter 2 Database Management Systems, R. Ramakrishnan and J. Gehrke 1

Overview of Database Design v Requirements Analysis – Understand what data is to be stored in DB, what applications must be built on top of it, and what operations are most frequent and subject to performance requirements. – Involve discussions with user groups, a study of current operating environment and how it is expected to change, analysis of any available documentation on existing applications v Conceptual DB Design – Develop high-level description of data to be stored in DB, along with constraints that are known to hold over this data (ER model) Database Management Systems, R. Ramakrishnan and J. Gehrke 2

Overview of Database Design v Logical DB Design – Choose a DBMS to implement our DB design, convert conceptual DB design into a DB schema in the model of the chosen DBMS, e. g. , convert an ER schema into relational DB schema (chap 3) v Schema Refinement – Analyze collection of relations obtained in our relational DB schema to identify potential problems (insert/delete/update anomalies), and to refine it (chap 19) v Physical DB Design – Consider typical expected workloads that our DB must support and further refine DB design (chap 20) Database Management Systems, R. Ramakrishnan and J. Gehrke 3

Overview of Database Design v Security Design – Identify different user groups and different roles played by various users. For each role, identify the parts of DB that they be able to access and the parts of DB they should not be allowed to access (chap 21) Database Management Systems, R. Ramakrishnan and J. Gehrke 4

Conceptual Database Design v 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. Database Management Systems, R. Ramakrishnan and J. Gehrke 5

ER Model Basics ssn name lot Employees v Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. v 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 (minimal set of attributes whose values uniquely identify an entity in the set). There could be more one candidate key; if so, we designate one of them as primary key Each attribute has a domain (set of possible values an attribute may take). Database Management Systems, R. Ramakrishnan and J. Gehrke 6

name ER Model Basics (Contd. ) ssn lot Employees v v dname budget did Works_In lot Employees since name ssn Departments supervisor subordinate Reports_To Relationship: Association among two or more entities. E. g. , Attishoo works in Pharmacy department. Relationship Set: Collection of similar relationships. – An n-ary relationship set R relates n entity sets E 1. . . En; each relationship in R involves entities e 1 E 1, . . . , en En u Same entity set could participate in different relationship sets, or in different “roles” in same set. Database Management Systems, R. Ramakrishnan and J. Gehrke 7

name ER Model Basics (Contd. ) ssn lot Employees v v dname budget did Works_In lot Employees since name ssn Departments supervisor subordinate Reports_To A relationship can also have descriptive attributes: Descriptive attributes are used to record information about the relationship, e. g. , since attribute associated with Works_In relationship Instance of a relationship: a set of relationships, e. g. , Figure 2. 3 Database Management Systems, R. Ramakrishnan and J. Gehrke 8

ER Model Basics (Cont. ) v Ternary relationship: a relationship involving three entities since name ssn lot dname did Employees Works_In 2 address Locations Database Management Systems, R. Ramakrishnan and J. Gehrke budget Departments capacity 9

Key Constraints since name ssn v v 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. dname lot Employees 1 -to-1 1 -to Many Database Management Systems, R. Ramakrishnan and J. Gehrke did Manages Many-to-1 budget Departments Many-to-Many 10

Participation Constraints v 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). u Every did value in Departments table must appear in a row of the Manages table (with a non-null ssn value!) since name ssn did lot Employees dname Manages budget Departments Works_In since Database Management Systems, R. Ramakrishnan and J. Gehrke 11

Weak Entities v 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-tomany relationship set (one owner, many weak entities). Weak entity set must have total participation in this identifying relationship set. name ssn lot Employees cost Policy Database Management Systems, R. Ramakrishnan and J. Gehrke pname age Dependents 12

name ssn ISA (`is a’) Hierarchies lot Employees v. As in C++, or other PLs, hourly_wages hours_worked ISA contractid attributes are inherited. v. If we declare A ISA B, every A Contract_Emps Hourly_Emps entity is also considered to be a B entity. v Overlap constraints: Can Joe be an Hourly_Emps as well as a Contract_Emps entity? (Allowed/disallowed) v Covering constraints: Does every Employees entity also have to be an Hourly_Emps or a Contract_Emps entity? (Yes/no) v Reasons for using ISA: – To add descriptive attributes specific to a subclass. – To identify entitities that participate in a relationship. Database Management Systems, R. Ramakrishnan and J. Gehrke 13

Overlap constraints: suppose there is another Senior_Emps subclass, and an employee can be both a Contract_Emps entity and a Senior_Emps entity; we denote this by writing ‘Contract_Emps OVERLAPS Senior_Emps’ Covering constraints: If every Motor_Vehicles entity have to be either a Motorboats entity or a Cars entity, then we write ‘Motorboats AND Cars COVER Motor_Vehicles’ Database Management Systems, R. Ramakrishnan and J. Gehrke 14

ssn Aggregation v 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. name lot Employees Monitors since started_on pid pbudget Projects until did Sponsors dname budget Departments * Aggregation vs. ternary relationship: v Monitors is a distinct relationship, with a descriptive attribute. v Also, can say that each sponsorship is monitored by at most one employee. Database Management Systems, R. Ramakrishnan and J. Gehrke 15

Conceptual Design Using the ER Model v Design choices: – – – v 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. Database Management Systems, R. Ramakrishnan and J. Gehrke 16

Entity vs. Attribute v v 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: u u 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). Database Management Systems, R. Ramakrishnan and J. Gehrke 17

Entity vs. Attribute (Contd. ) v v name from to dname Works_In 2 does not ssn lot did budget allow an employee to Departments Works_In 2 Employees work in a department for two or more periods. Similar to the problem of wanting to record several addresses for an name dname employee: we want to ssn lot did budget record several values of the Works_In 3 Departments Employees descriptive attributes for each instance of this Duration to from relationship. Database Management Systems, R. Ramakrishnan and J. Gehrke 18

Entity vs. Relationship v v First ER diagram OK if since dbudget name a manager gets a ssn lot did separate discretionary budget for each dept. Employees Manages 2 What if a manager gets a discretionary budget that covers all name ssn managed depts? lot did – Redundancy of dbudget, which is stored for each dept managed by the manager. Employees Misleading: suggests dbudget tied to managed dept. Manages 3 dname budget Departments since apptnum Database Management Systems, R. Ramakrishnan and J. Gehrke Mgr_Appts dbudget 19

Binary vs. Ternary Relationships ssn v If each policy is owned by just 1 employee: – v Key constraint on Policies would mean policy can only cover 1 dependent! What are the additional constraints in the 2 nd diagram? name pname lot Employees Policies policyid ssn name Dependents Covers Bad design age cost pname lot age Dependents Employees Purchaser Beneficiary Better design Database Management Systems, R. Ramakrishnan and J. Gehrke policyid Policies cost 20

Binary vs. Ternary Relationships (Contd. ) v v 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? Database Management Systems, R. Ramakrishnan and J. Gehrke 21

Summary of Conceptual Design v Conceptual design follows requirements analysis, – v ER model popular for conceptual design – v v v Yields a high-level description of data to be stored 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. Database Management Systems, R. Ramakrishnan and J. Gehrke 22

Summary of ER (Contd. ) v 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. Database Management Systems, R. Ramakrishnan and J. Gehrke 23

Summary of ER (Contd. ) v 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: – v Entity vs. attribute, entity vs. relationship, binary or nary 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. Database Management Systems, R. Ramakrishnan and J. Gehrke 24