Critically Compare Different Data Models Schemas

Data Models & Schemas

Critically Compare Different Data Models SchemasSuneth Fernando

Objectives

Why data models are important

About the basic data-modeling building blocks

What business rules are and how they influence database design

How the major data models evolved

How data models can be classified by level of abstraction

The Importance of Data Models

Data models

  • Relatively simple representations, usually graphical, of complex real-world data structures
  • Facilitate interaction among the designer, the applications programmer, and the end user

The Importance of Data Models

(continued)

End-users have different views and needs for data

Data model organizes data for various users

Data Model Basic Building Blocks

Entity – anything about which data are to be collected and stored

Attribute – a characteristic of an entity

Relationship – describes an association among entities

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

Constraint – a restriction placed on the data

Business Rules

Brief, precise, and unambiguous descriptions of a policies, procedures, or principles within a specific organization

Apply 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

Business Rules (continued)

Must be rendered in writing

Must be kept up to date

Sometimes are external to the organization

Must be easy to understand and widely disseminated

Describe characteristics of the data as viewed by the company

Discovering Business Rules

Sources of Business Rules:

Company managers

Policy makers

Department managers

Written documentation

  • Procedures
  • Standards
  • Operations manuals

Direct interviews with end users

Translating Business Rules into Data Model Components

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

Promote creation of an accurate data model

Discovering Business Rules (continued)

Generally, nouns translate into entities

Verbs translate into relationships among entities

Relationships are bi-directional

The Evolution of Data Models

(continued)

Hierarchical

Network

Relational

Entity relationship

Object oriented (OO)

The Hierarchical Model

Developed in the 1960s to manage large amounts of data for complex manufacturing projects

Basic logical structure is represented by an upside-down “tree”The Hierarchical Model (continued)

The Hierarchical Model (continued)

The hierarchical structure contains levels, or segments

Depicts a set of one-to-many (1:M) relationships between a parent and its children segments

  • Each parent can have many children
  • each child has only one parent
The Hierarchical Model (continued)

Advantages

  • Many of the hierarchical data model’s features formed the foundation for current data models
  • Its database application advantages are replicated, albeit in a different form, in current database environments
  • Generated a large installed (mainframe) base, created a pool of programmers who developed numerous tried-and-true business applications
The Hierarchical Model (continued)

Disadvantages

  • Complex to implement
  • Difficult to manage
  • Lacks structural independence
  • Implementation limitations
  • Lack of standards
The Network Model

Created to

  • Represent complex data relationships more effectively
  • Improve database performance
  • Impose a database standard

Conference on Data Systems Languages (CODASYL)

Database Task Group (DBTG)

Critically Compare Different Data Models Schemas

The Network Model (continued)

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 Management Language (DML)

  • Defines the environment in which data can be managed
The Network Model (continued)

Schema Data Definition Language (DDL)

  • Enables database administrator to define schema components Subschema DDL
  • Allows application programs to define database components that will be used

DML

  • Works with the data in the database

The Network Model (continued)

Resembles hierarchical model

Collection of records in 1:M relationships

Set

  • Relationship
  • Composed of at least two record types
  • Owner
  • Equivalent to the hierarchical model’s parent
  • Member
  • Equivalent to the hierarchical model’s child

The Network Model (continued)

Disadvantages

  • Too cumbersome
  • The lack of ad hoc query capability put heavy pressure on programmers
  • Any structural change in the database could produce havoc in all application programs that drew data from the database
  • Many database old-timers can recall the interminable information delays

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

The Relational Model (continued)

Relational Database Management System (RDBMS)

Performs same basic functions provided by hierarchical and network

DBMS systems, in addition to a host of other functions

Most important advantage of the RDBMS is its ability to hide the complexities of the relational model from the user

The Relational Model (continued)

Table (relations)

  • Matrix consisting of a series of row/column intersections
  • Related to each other through sharing a common entity characteristic Relational diagram
  • Representation of relational database’s entities, attributes within those entities, and relationships between those entities

The Relational Model (continued)

Relational Table

  • Stores a collection of related entities
  • Resembles a file

Relational table is purely logical structure

  • How data are physically stored in the database is of no concern to the user or the designer
  • This property became the source of a real database revolution

The Relational Model (continued)

Rise to dominance due in part to its powerful and flexible query language

Structured Query Language (SQL) allows the user to specify what must be done without specifying how it must be done

SQL-based relational database application involves:

  • User interface
  • A set of tables stored in the database
  • SQL engine

 

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

The Entity Relationship Model

(continued)

Entity relationship diagram (ERD)

  • Uses graphic representations to model database components
  • 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

  • Diamond connected to related entities through a relationship line

The Entity Relationship Model (continued)

The Object Oriented Model

Modeled both data and their relationships in a single structure known as an object

Object-oriented data model (OODM) is the basis for the objectoriented database management system (OODBMS)

OODM is said to be a semantic data model

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The Object Oriented Model (continued)

Object described by its factual content

  • Like relational model’s entity

Includes information about relationships between facts within object, and relationships with other objects

  • Unlike relational model’s entity

Subsequent OODM development allowed an object to also contain all operations

Object becomes basic building block for autonomous structures

The Object Oriented Model (continued)

Object is an 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

The Object Oriented Model (continued)

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

Database Models and the Internet

Internet drastically changed role and scope of database market

OODM and ERDM-O/RDM have taken a backseat to development of databases that interface with Internet

Dominance of Web has resulted in growing need to manage unstructured information

Data Models: A Summary

Each new data model capitalized on the shortcomings of previous models Common characteristics:

  • Conceptual simplicity without compromising the semantic completeness of the database
  • Represent the real world as closely as possible
  • Representation of real-world transformations (behavior) must comply with consistency and integrity characteristics of any data model

Data Models: A Summary (continued)

Degrees of Data Abstraction

(Database Schema)

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

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Degrees of Data Abstraction (continued)

American National Standards Institute (ANSI) Standards Planning and

Requirements Committee (SPARC)

  • Defined a framework for data modeling based on degrees of data abstraction(1970s):
  • External
  • Conceptual
  • Internal

Degrees of Data Abstraction (continued)

HND in

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

45e External Model (continued)

Advantages:

  • Easy to identify specific data required to support each business unit’s operations
  • Facilitates designer’s job by providing feedback about the model’s adequacy
  • Creation of external models helps to ensure security constraints in the database design
  • Simplifies application program development

The External Model (continued)

The Conceptual Model

Represents global view of the entire database

Representation of data as viewed by the entire organization

Basis for identification and high-level description of main data objects, avoiding details

Most widely used conceptual model is the entity relationship (ER) model

The Conceptual Model (continued)

The Conceptual Model (continued)

Provides a relatively easily understood macro level view of data environment

Independent of both software and hardware

  • Does not depend on the DBMS software used to implement the model
  • Does not depend on the hardware used in the implementation of the model
  • Changes in either hardware or 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

Maps the conceptual model to the DBMS

The Internal Model (continued)

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 Summary

A data model is a (relatively) simple abstraction of a complex realworld data environment

Basic data modeling components are:

  • Entities
  • Attributes
  • Relationships
  • Constraints

Summary (continued)

Hierarchical model

  • Depicts a set of one-to-many (1:M) relationships between a parent and its children segments

Network data model

  • Uses sets to represent 1:M relationships between record types

Relational model

  • Current database implementation standard
  • ER model is a popular graphical tool for data modeling that complements the relational model

Summary (continued)

Object is basic modeling structure of object oriented data model

The relational model has adopted many objectoriented extensions to become the extended relational data model (ERDM)

Data modeling requirements are a function of different data views (global vs. local) and level of

data abstraction