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
- 20. The Entity Relationship Model 20
- 21. The Entity Relationship Model 21
- 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
- 30. 30 Thank you