Chapter 2 Introduction to Modeling

Objectives: to understand • reduces complexities of • Data modeling and why data models are important • Designers, programmers, and end users see data • The basic data-modeling building blocks in different ways • What business rules are and how they influence • Different views of same data lead to that do not reflect organization’s operation • How the major data models evolved historically • Various degrees of data help • How data models can be classified by level of reconcile varying views of same data abstraction

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Data Modeling and Data Models The Importance of Data Models

• Model: an abstraction of a real-world object or • Facilitate interaction among the designer, the event applications programmer, and the end user – Useful in complexities of the real- • End users have different views and needs for data world environment • organizes data for various users • Data models • Data model is a - an abstraction – Relatively simple representations of complex real- • It’s a graphical collection of logical constructs world data structures representing the and relationships • Often graphical within the database. • Creating a Data model is iterative and progressive – Cannot draw required data out of the data model – An implementation model would represent how the data are represented in the database. CS275 Fall 2010 3 CS275 Fall 2010 4

1 Data Model Basic Building Blocks Business Rules Terminology • Entity : anything about which data are to be • Descriptions of policies or principles within an collected and stored organization • Attribute : a characteristic of an entity • Description of operations or procedures, to • Relationship : describes an association among create/enforce actions within an organization’s entities environment – One-to-many (1:M) relationship – Must be in writing and kept up to date – Many-to-many (M:N or M:M) relationship – Must be easy to understand and widely disseminated – Sometimes externally defined, i.e. government – One-to-one (1:1) relationship regulations. • Constraint : a restriction placed on the data • These describe characteristics of data as viewed by the company

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Discovering Business Rules Importance of Business Rules

• Sources of business rules: • Standardize company’s of data – Company managers • Useful as a communications tool between users – Policy makers and designers – Department managers • Allows the designer to – Written documentation – understand the nature, role, and scope of data • Procedures – understand business processes • Standards – develop appropriate relationship participation • Operations manuals rules and constraints – Direct interviews with end users • Promotes the creation of an accurate data model

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2 Translating Business Rules into Data Naming Conventions Model Components • Naming occurs during translation of business • Generally, nouns translate into entities rules to data model components • Verbs translate into relationships among entities • Names should make the object unique and • Relationships are bidirectional distinguishable from other objects • Two questions to identify the relationship type: • Names should also be descriptive of objects in the – How many instances of B are related to one environment and be familiar to users instance of A? • Proper naming: – How many instances of A are related to one – Facilitates communication between parties instance of B? – Promotes self-documentation

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Evolution of Data The Hierarchical Model Implementation Models • Hierarchical • The hierarchical model was developed in the 1960s – Logically represented by an upside down tree to manage large amounts of data for manufacturing • Each parent can have many children projects • Each child has only one parent • Basic logical structure is represented by an upside- down “tree” • Network • Hierarchical structure contains levels or segments • Relational – Segment analogous to a record type • Object oriented – Set of one-to-many relationships between segments • Hybrid, XML • Example – manufacturing a car from components (a,b,or c), each made of subassemblies (1,2,or3), each having parts (x,y,&z) ....(tree structure)

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3 Hierarchical Structure Hierarchical Structure

• Each parent can have many children • Each child has only one parent • Tree is defined by path that traces parent segments to child segments, beginning from the left • Hierarchical path – Ordered sequencing of segments tracing hierarchical structure • Preorder traversal or hierarchic sequence – “Left-list” path

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The Hierarchical Model The Hierarchical Model

• GUAM (Generalized Update Access Method) • Advantages • Disadvantages – Based on the recognition that the many smaller – Conceptual simplicity – Complex parts would come together as components of still implementation larger components – Database security – Data independence – Difficult to manage • (IMS) – Database integrity – Lacks structural – World’s leading mainframe hierarchical database – Efficiency independence system in the 1970s and early 1980s – Complex applications • TCDMS/ADABAS – jointly developed by IBM and programming and use Lane County – Implementation limitations – Lack of standards

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4 The The Network Model

• The network model was created to represent • Collection of records in 1:M relationships complex data relationships more effectively than • A Set is a relationship and composed of two the hierarchical model record types: – Improves database performance – Owner: Equialent to the hierarchical model’s – Imposes a database standard parent – Represent complex data relationships more – Member: Equivalent to the hierarchical model’s effectively – such as child w/ multiple parents child • Conference on Data Languages (CODASYL) • American National Standards Institute (ANSI) • Database Task Group (DBTG)

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The Network Model Components The Network Model

• still used today: • Advantages: • Disadvantages of the – Schema: Conceptual organization of entire – Conformance to network model: database as viewed by the database administrator standards – System complexity – Subschema : Database portion “seen” by the – Handled more – Lack of ad hoc query application programs relationship types capability placed – language (DML): Defines the – Data access flexibility burden on environment in which data can be managed programmers to – (DDL): Enables the generate code for reports administrator to define the schema components – Structural change in the database could produce havoc in all

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5 The Relational

• Developed by E.F. Codd (IBM) in 1970 • A Relational table is a purely logical structure • Relational models were considered impractical in – How data are physically stored in the database is the 1970’s. of no concern to the user or the designer. • Model was conceptually simple at expense of • Stores a collection of related entities computer overhead – Resembles a file • Relational table is purely logical structure • Table (relations) – How data are physically stored in the database is – Matrix consisting of a series of row/column of no concern to the user or the designer intersections – This is the source of a real database – Each row in a relation is called a tuple revolution – Related to each other by sharing a common entity characteristic CS275 Fall 2010 21 CS275 Fall 2010 22

The Relational Model Components

• Relational data management system (RDBMS) – Performs same functions provided by hierarchical model, but hides complexity from the user • Relational schema/ – Visual representation of ’s entities, attributes within those entities, and relationships between those entities • Relational diagram – Representation of entities, attributes, and relationships • Relational table stores collection of related entities. CS275 Fall 2010 23 CS275 Fall 2010

6 The Relational DBMS Application The Relational Implementation Model

• SQL-based relational database application • Advantages • Disadvantages involves three parts: – Structural – Substantial hardware – User interface independence and system overhead • Allows end user to interact with the data – Improved conceptual simplicity – Can facilitate poor – Set of tables stored in the database – Easier database design, design and • Each table is independent from another implementation, implementation • Rows in different tables are related based on management, and use – May promote “islands common values in common attributes – Ad hoc query of information” problems – SQL “engine” capability (SQL) – Powerful database • Executes all queries management system

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Logical/Conceptual Model The Entity Relationship Model The Entity Relationship Model • Widely accepted standard for data modeling • Entity instance (or occurrence) is row in table • Introduced by Chen in 1976 • Entity set is collection of like entities • Graphical representation of entities and their • Connectivity labels types of relationships relationships in a database structure • Relationships are expressed using Chen notation • Entity relationship diagram (ERD) – Relationships are represented by a diamond – Uses graphic representations to model database – Relationship name is written inside the diamond components – Entity is mapped to a relational table • Crow’s Foot notation used as design standard in this book

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7 Logical/Conceptual Model The Object-Oriented (OO) Model

• Models both data and relationships contained in a single structure known as an object • OODM (object-oriented data model) is the basis for OO-DBMS () • An object is described by its factual content: – Are self-contained: a basic building-block for autonomous structures – Is an abstraction of a real-world entity – Contains information about relationships between facts within the object and with other objects.

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The Object-Oriented (OO) Model

• An Object is the logical abstraction or basic building block for autonomous structures – Attributes describe the properties of an object – Objects that share similar characteristics are grouped in classes – Classes are organized in a class hierarchy – Inheritance: an object inherits methods and attributes of parent class – UML - Unified is used to graphically model a system • based on OO concepts that describe and

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8 Logical Models: Newer Data Models: Object/Relational Object Oriented Model • Extended relational data model (ERDM) • Advantages • Disadvantages – Semantic data model developed in response to – Adds semantic content – Slow pace of OODM increasing complexity of applications – Visual presentation standards includes semantic development – Includes many of OO model’s best features content – Complex navigational – Often described as an object/relational database – Database integrity data access management system (O/RDBMS) – Both structural and – Steep learning curve – Primarily geared to business applications data independence – High system overhead slows transactions – Lack of market penetration

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Newer Data Models: XML The Future of Data Models

• The Internet revolution created the potential to • Hybrid DBMSs exchange critical business information – Retain advantages of relational model • Dominance of Web has resulted in growing need – Provide object-oriented view of the underlying to manage unstructured information data • In this environment, Extensible • SQL data services – ‘Cloud ’ (XML) emerged as the de facto standard – Store data remotely without incurring expensive • Current support XML hardware, software, and personnel costs – XML: the standard protocol for data exchange – Companies operate on a “pay-as-you-go” system among systems and Internet services

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9 The Development of Data Models Data Models: A Summary

• Each new data model capitalized on the shortcomings of previous models • Common characteristics: – Conceptual simplicity with semantic completeness – Represent the real world as closely as possible – Real-world transformations (behavior) must comply with consistency and integrity characteristics • Some models better suited for some tasks

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SPARC Framework : The SPARC External Model Degrees of Data Abstraction • Represents the End users’ view of the data • Database designer starts with abstracted view, environment then adds details • ER diagrams represent external views • ANSI Standards Planning and • External schema: specific representation of an Committee (SPARC) external view – Defined a framework for data modeling based on – Entities degrees of data abstraction (1970s): 1. External – Relationships 2. Conceptual – Processes 3. Internal – Constraints

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10 External Models showing The External Model two different Users

• End users’ view of the data environment Conceptual Model • Requires that the modeler subdivide set of requirements and constraints into functional modules that can be examined within the framework of their external models • Advantages: – Easy to identify specific requirements to support each business unit’s operations – Facilitates designer’s job by providing feedback about the model’s adequacy – Ensures security constraints in database design – Simplifies application program development CS275 Fall 2010 41 CS275 Fall 2010 42

The SPARC Conceptual Model The Conceptual Model

• Represents global view of the entire database Advantages – All external views integrated into single global • Provides a relatively easily understood macro view: level view of data environment • Representation of data as viewed by high-level • Independent of both software and hardware managers – Does not depend on the DBMS software used to • ER Diagram graphically represents the implement the model conceptual schema – Does not depend on the hardware used in the – ER model most widely used conceptual model implementation of the model • Basis for identification and description of main – Changes in hardware or software or do not affect data objects, avoiding details database design at the conceptual level

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11 The SPARC Internal Model

• Representation of the database as “seen” by the DBMS – Maps the Conceptual model to the DBMS • Internal schema depicts a specific representation of an internal model • Depends on specific database software – Change in DBMS software requires internal model be changed • Logical independence: change internal model without affecting conceptual model

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The Physical Model Summary

• Operates at lowest level of abstraction • A data model is an abstraction of a complex real- – Describes the way data are saved on storage world data environment media such as disks or tapes • Basic data modeling components: – Software and hardware dependent – Entities • Requires the definition of physical storage and – Attributes data access methods – Relationships • Relational model aimed at logical level – Constraints – Does not require physical-level details • Business rules identify and define basic modeling • Physical independence: changes in physical components model do not affect internal model

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12 Summary Summary

• Hierarchical model • Object-oriented data model: object is basic – Set of one-to-many (1:M) relationships between a modeling structure parent and its children segments • Relational model adopted object-oriented • Network data model extensions: extended relational data model – Uses sets to represent 1:M relationships between (ERDM) record types • OO data models depicted using UML • Relational model • Data-modeling requirements are a function of – Current database implementation standard different data views and abstraction levels – ER model is a tool for data modeling – Three SPARC abstraction levels: external, • Complements relational model conceptual, internal

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