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Data Models - Department of Computer Science & Engineering

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Why data models are important? About the basic data-modeling building blocks. How the major data models evolved? How data models can be classified by level of abstraction?

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Data Models Department of Computer Science & Engineering
Objectives • Why data models are important? • About the basic data-modeling building blocks. • How the major data models evolved? • How data models can be classified by level of abstraction? 2
The Importance of Data Models • Data models • Relatively simple representations, usually graphical, of complex real-world data structures. • Facilitate interaction among the designer, the application programmer, and the end user • End-users have different views and needs for data • Data model organizes data for various users 3
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. 4
The Evolution of Data Models • Hierarchical • Network • Relational • Entity relationship • Object oriented (OO) 5
The Hierarchical Model • Developed in the 1960s to manage large amounts of data for complex manufacturing projects 6
The Hierarchical Model • 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 7
The Network Model • Created to • Represent complex data relationships more effectively • Improve database performance • Impose a database standard • Schema • Conceptual organization of entire database as viewed by the database administrator 8
The Network Model (continued) • 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 9
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 10
The Network Model (continued) 11
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 12
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 13
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 14
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 15
The Relational Model (continued) 16
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 17
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 18
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 19
The Entity Relationship Model 20
The Entity Relationship Model 21
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 object-oriented database management system (OODBMS) • OODM is said to be a semantic data model 22
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 • Object becomes basic building block for autonomous structures 23
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 24
The Object Oriented Model (continued) 25
Data Models: A Summary 26
Degrees of Data 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 27
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 28
Degrees of Data Abstraction 29
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Data Models - Department of Computer Science & Engineering

  • 1. Data Models Department of Computer Science & Engineering
  • 2. Objectives • Why data models are important? • About the basic data-modeling building blocks. • How the major data models evolved? • How data models can be classified by level of abstraction? 2
  • 3. The Importance of Data Models • Data models • Relatively simple representations, usually graphical, of complex real-world data structures. • Facilitate interaction among the designer, the application programmer, and the end user • End-users have different views and needs for data • Data model organizes data for various users 3
  • 4. 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. 4
  • 5. The Evolution of Data Models • Hierarchical • Network • Relational • Entity relationship • Object oriented (OO) 5
  • 6. The Hierarchical Model • Developed in the 1960s to manage large amounts of data for complex manufacturing projects 6
  • 7. The Hierarchical Model • 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 7
  • 8. The Network Model • Created to • Represent complex data relationships more effectively • Improve database performance • Impose a database standard • Schema • Conceptual organization of entire database as viewed by the database administrator 8
  • 9. The Network Model (continued) • 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 9
  • 10. 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 10
  • 11. The Network Model (continued) 11
  • 12. 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 12
  • 13. 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 13
  • 14. 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 14
  • 15. 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 15
  • 16. The Relational Model (continued) 16
  • 17. 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 17
  • 18. 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 18
  • 19. 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 19
  • 22. 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 object-oriented database management system (OODBMS) • OODM is said to be a semantic data model 22
  • 23. 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 • Object becomes basic building block for autonomous structures 23
  • 24. 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 24
  • 25. The Object Oriented Model (continued) 25
  • 26. Data Models: A Summary 26
  • 27. Degrees of Data 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 27
  • 28. 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 28
  • 29. Degrees of Data Abstraction 29