CPL

Chapter 2: Data Models

Note There's minimal coverage of Hierarichcal and Network models as I consider them of historical importance only

Topics

* Business rules
* Data models
o About the basic data-modeling building blocks
o How the major data models evolved, and their advantages and disadvantages
o How data models can be classified by level of abstraction

Business Rules

* A policy, procedure, or principle within a specific organization's environment
* Applies to any organization that stores and uses data to generate information
* Description of operations that help to create and enforce actions within that organization's environment
* Sometimes are external to the organization
* Describe characteristics of the data as viewed by the company

Business Rules (cont)

* Must be rendered in writing
* Brief, precise, unambiguous
* Must be kept up to date
* Must be easy to understand and widely disseminated

Sources of Business Rules

* Company managers
* Policy makers
* Department managers
* Written documentation
o Procedures
o Standards
o Operations manuals
* Direct interviews with end users

Importance of Business Rules

* Promote creation of an accurate data model
* Standardize company's view of data
* Constitute a communications tool between users and designers
* Allow designer to understand the nature, role, and scope of data
* Allow designer to understand business processes
* Allow designer to develop appropriate relationship participation rules and constraints

The Importance of Data Models

* Good database design uses an appropriate data model as its foundation
* End-users have different views and needs for data
* Data model organizes data for various users

Data model
Relatively simple representation, usually graphical, of complex real-world data structures
Communications tool to facilitate interaction among the designer, the applications programmer, and the end user

Data Model Building Blocks

Entity
Anything about which data are to be collected and stored
Attribute
A characteristic of an entity (e.g. last name)
Relationship
an association among (two or more) entities

* One-to-many (1:M) relationship
* Many-to-many (M:N or M:M) relationship
* One-to-one (1:1) relationship

The Evolution of Data Models

* Hierarchical
* Network
* Relational
* Entity relationship
* Object oriented

Crucial Database Components

Schema
Conceptual organization of entire database as viewed by the database administrator
Subschema
Defines database portion "seen" by the application programs that actually produce the desired information from data contained within the database
Data Definition Language (DDL)
Define data characteristics and data structure in order to manipulate the data

DML Concepts

Schema Data Definition Language (DDL)
Enables database administrator to define schema components and relationships
Subschema DDL
Allows application programs to define database components that will be used
DML
Manipulates database contents

The Relational Model

* Developed by Codd (IBM) in 1970
* Considered ingenious but impractical in 1970
* Conceptually simple
* Computers lacked power to implement the relational model
* Today, microcomputers can run sophisticated relational database software

Relational Model: Basic Structure

* Relational Database Management System (RDBMS)
* Most important advantage of the RDBMS is its ability to let the user/designer operate in a human logical environment

Relation (Table)
Matrix consisting of a series of row/column intersections
Related to each other by sharing a common entity characteristic
Relational schema
Representation of relational database's entities, attributes within those entities, and relationships between those entities
Represented as DDL or Visually

Alternative Terminology
Relational Common Traditional
relation table file
tuple row record
attribute cell field
instance value value
Relational Schema

Relational Schema
Linking Relational Tables

Fig 2.4 Linking Relations
Advantages of the Relational Model

* Structural independence
* Improved conceptual simplicity
* Easier database design, implementation, management, and use
* Ad hoc query capability
* Powerful database management system

Disadvantages of the Relational Model

* Substantial hardware and system software overhead
* May not fit all business models
* Can facilitate poor design and implementation
* May promote "islands of information" problems

The Entity Relationship Model

* Widely accepted and adapted graphical tool for data modeling
* Introduced by Chen in 1976
* Graphical representation of entities and their relationships in a database structure

Basic Structure of the Entity Relationship Model

* Entity relationship diagram (ERD)
o Uses graphic representations to model database components
o Entity is mapped to a relational table
* Entity instance (or occurrence) is row in table
* Entity set is collection of like entities
* Connectivity labels types of relationships

Relationships: the Basic Chen ERD

Note: we will be using mostly CrowsFoot notation

Fig 2.6 Chen ERD
Crow's Foot ERD

Fig 2.7 Crow's Foot ERD
Advantages of E.R. Model

* Exceptional conceptual simplicity
* Visual representation
* Effective communication tool
* Integrated with the relational data model

Disadvantages of E.R. Model

* Limited constraint representation
* Limited relationship representation
* No data manipulation language
* Loss of information content

The Object Oriented Model

* Semantic data model (SDM) developed by Hammer and McLeod in 1981
* Modeled both data and their relationships in a single structure known as an object
* Basis of object oriented data model (OODM)
* OODM becomes the basis for the object oriented database management system (OODBMS)

Object Oriented Model (cont)

* Object is described by its factual content
o Like relational model's entity
* Includes information about relationships between facts within object and relationships with other objects
o Unlike relational model's entity
* Subsequent OODM development allowed an object to also contain operations (aka methods)
* Object becomes basic building block for autonomous structures

Developments that Boosted OODM

* Growing costs put a premium on code reusability
* Complex data types and system requirements became difficult to manage with a traditional RDBMS
* Became possible to support increasingly sophisticated transaction & information requirements
* Ever-increasing computing power made it possible to support the large computing overhead required

OODM: Basic Structure

* Object: abstraction of a real-world entity
* Attributes describe the properties of an object
* Objects that share similar characteristics are grouped in classes
* Classes are organized in a class hierarchy
* Inheritance is the ability of an object within the class hierarchy to inherit the attributes and methods of classes above it

Comparison of OO and ER Models

Fig 2.6 Comparison between OO and ER Model
OO Model Advantages

* Adds semantic content
* Visual presentation includes semantic content
* Database integrity
* Both structural and data independence

OO Model Disadvantages

* Slow pace of OODM standards development
* Complex navigational data access
* Steep learning curve
* High system overhead slows transactions
* Lack of market penetration
* Current relational DBMS's have many OO characteristics

Other Models: Extended Relational Data Model (ERDM)

* Semantic data model developed in response to increasing complexity of applications
* DBMS based on the ERDM often described as an object/relational database management system (O/RDBMS)
* Primarily geared to business applications

Date's Objection to ERDM

* Given proper support for domains, relational data models are quite capable of handling complex data
o Therefore, capability that is supposedly being extended is already there
* O/RDM label is not accurate because the relational data model's domain is not an object model structure

Data Models: A Summary

* Each new data model was intended to overcome the shortcomings of previous models
* Common characteristics:
o Conceptual simplicity without compromising the semantic completeness of the database
o Represent the real world as closely as possible
o Representation of real-world transformations (behavior) must be in compliance with consistency and integrity characteristics of any data model

Degrees of Abstraction

* Way of classifying data models
* Many processes begin at high level of abstraction and proceed to an ever-increasing level of detail
* Designing a usable database follows the same basic process

ANSI/SPARC Data Models

American National Standards Institute/Standards Planning and Requirements Committee (ANSI/SPARC)
Classified data models according to their degree of abstraction (1970s):

* Conceptual
* External
* Internal

Data Abstraction Levels

Data Abstraction Levels
The Conceptual Model

* Represents global view of the database
* Enterprise-wide representation of data as viewed by high-level managers
* Basis for identification and description of main data objects, avoiding details
* Most widely used conceptual model is the entity relationship (ER) model

Conceptual Model Tiny College

Advantages of Conceptual Model

* Provides a relatively easily understood macro level view of data environment
* Independent of both software and hardware
o Does not depend on the DBMS software used to implement the model
o Does not depend on the hardware used in the implementation of the model
o Changes in either the hardware or the DBMS software have no effect on the database design at the conceptual level

The Internal Model

* Representation of the database as �seen� by the DBMS
* Adapts the conceptual model to the DBMS
* Software dependent
* Hardware independent

The External Model

* End users� view of the data environment
* Requires that the modeler subdivide set of requirements and constraints into functional modules that can be examined within the framework of their external models
* Good design should:
o Consider such relationships between views
o Provide programmers with a set of restrictions that govern common entities

Division of Conceptual Model into External Models

Fig 2-13
Advantages of External Models

* Use of database subsets makes application program development much simpler
o Facilitates designer�s task by making it easier to identify specific data required to support each business unit�s operations
o Provides feedback about the conceptual model�s adequacy
* Creation of external models helps to ensure security constraints in the database design

External Models for Tiny College

The Physical Model

* Operates at lowest level of abstraction, describing the way data are saved on storage media such as disks or tapes
* Software and hardware dependent
* Requires that database designers have a detailed knowledge of the hardware and software used to implement database design

Levels of Data Abstraction

Summary

* A good DBMS will perform poorly with a poorly designed database
* A data model is a (relatively) simple abstraction of a complex real-world data-gathering environment
* Basic data modeling components are
o Entities
o Attributes
o Relationships