Feb 4 Recap of Jan 30 class Data Models E R and Relational and some others of mostly historical interest We examined the E R model Entities Relationships and Attributes Diagram based model Talked about Keys Some special cases for the E R model Today we ll examine two enhancements of the E R model that allow representation of some hierarchical information then move on to the Relational database model Specialization Generalization ISA Hierarchy This is a way to represent entity complexity specialization top down refinement of entities with distinct attributes Entity type BANK ACCOUNT might be subdivided into related but different types CHECKING ACCT and SAVINGS ACCT generalization bottom up abstraction of common attributes Course types DATABASE SYSTEM and NETWORK all have common attribute project From them we can abstract a new course type PRACTICAL COURSE other common course attributes are included e g course number ISA Hierarchy Example Top down Refinement Account entity with attributes balance and number additional complexity we want to represent two subtypes of account Savings Account with attribute Interest Rate Checking Account with attribute Overdraft Limit ISA Hierarchy Example Bottom up Abstraction Three related entities with similar attribute project we abstract a new type of super entity Practical Course and link the three entities as subtypes other shared attributes e g course number are also promoted to the upper level entity Aggregation Part of Hierarchy This is a way to represent relationship complexity relationships among relationships are not supported by the E R model often we want to model lower level relationships differently Groups of entities and relationships can be abstracted into higher level entities Part of Hierarchy Example Entities driver car tires doors engine seats piston valves Relationship drives is insufficient to model the complexity of this system Part of relationships allow abstraction into higher level entities piston and valves as parts of engine engine tires doors seats aggregated into car Mapping an E R Schema to Tables Motivation translating E R database designs into Relational designs Both models are abstract logical representations of a real world enterprise Both models employ similar design principles Converting an E R diagram to tables is the way we translate an ER schema to a Relational schema Later on we ll examine how to convert a Relational schema to an E R schema Mapping an E R Schema to Tables 2 Strong Entity E with primary key PK and attributes A B E PK A B Weak Entity F with non primary key WK and attributes C D depending upon E above for primary key F PK WK C D Relationship R with attributes L M and associating Entities E with primary key PK E2 PK2 E3 PK3 R PK PK2 PK3 L M Relationships between weak entities and the strong one on which they are dependent usually do not require representation because it is usually a many one relationship with no attributes on the relationship they are on the weak entity and so the resulting table R PK WK is a subset of the weak entity itself Table Details The whole table represents a single Entity Set or Relationship Set Each entry row in the table corresponds to a single instance member in that set For an Entity Set each column in the table represents an attribute in the E R diagram For a Relationship Set each column in the table represents either an attribute of the Relationship or one of the parts of the primary key of the Entity Sets it associates Mapping an E R Schema to Tables 3 ISA relationships choose either to Represent the super class entity then represent each subclass with the primary key of the super class and its own attribute set This is very similar to the way weak entities are treated Or map the subclasses to separate relations and ignore the whole super class This is good when the subclasses partition the whole superclasses between them the subclasses are disjoint and the union of the subclasses covers the whole super class Aggregate part of relationship Translation is straightforward just treat the aggregate as an entity and use the methods defined above With last week s lecture this covers the material of chapter 2 Relational Database Model Most popular logical data model Relations also called tables represent both Entity Sets and Relationship Sets Attributes form the columns of the table column and attribute are synonymous Each row represents a single entity or relationship called a row or tuple Each instance of an attribute takes values from a specific set called the domain of the column the domain defines the type Relational Database Model cont A relation schema is made up of the name and attributes of a relation with their underlying domains A database schema is a set of all relation schemas The notions of keys primary keys superkeys are all as previously described Query Languages a language in which a user requests information from the database a higher level language than standard programming languages query languages may be procedural or non procedural procedural languages specify a series of operations on the database to generate the desired result non procedural languages do not specify how the information is generated most commercial relational database systems offer a query language that includes procedural and non procedural elements Relational Algebra procedural query language set of operators that map one or more relations into another relation closed algebraic system best feature operations on operations form relational algebraic expressions two types of operations set theoretic and database specific Relational Algebra Operations database specific horizontal selection vertical projection join outer join semijoin division set operators union difference intersection cartesian cross product Example Relations EMP ename salary Gary 30K Shirley 35K Christos 37K Robin 22K Uma 30K Tim 12K dept toy candy shoe toy shoe null DEPT dept candy toy men shoe floor 1 2 2 1 mgr Irene Jim John George Database Specific Operators horizontal selection picks a subset of the rows vertical projection picks a subset of the columns join creates a new relation table out of two equijoin based upon equality of attributes natural join equijoin plus projection to eliminate duplicated columns Set Operators union both relations must be union compatible same degree and same domains set difference both relations must be union compatible as above intersection same deal cartesian cross product
View Full Document
Unlocking...