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Data Models.ppt
- 1. Data Models
- 2. 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 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 applications programmer, and the end user 3
- 4. The Importance of Data Models (continued) End-users have different views and needs for data Data model organizes data for various users 4
- 5. 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 5
- 6. The Evolution of Data Models (continued) Hierarchical Network Relational Entity relationship Object oriented (OO) 6
- 7. 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” 7
- 8. The Hierarchical Model (continued) 8
- 9. 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 9
- 10. 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 10
- 11. The Hierarchical Model (continued) Disadvantages Complex to implement Difficult to manage Lacks structural independence Implementation limitations Lack of standards 11
- 12. 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) 12
- 13. 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 13
- 14. 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 14
- 15. 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 15
- 16. The Network Model (continued) 16
- 17. 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 17
- 18. 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 18
- 19. 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 19
- 20. 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 20
- 21. 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 21
- 22. The Relational Model (continued) 22
- 23. The Relational Model (continued) 23
- 24. 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 24
- 25. 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 25
- 26. 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 26
- 27. The Entity Relationship Model (continued) 27
- 28. The Entity Relationship Model (continued) 28
- 29. 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 29
- 30. 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 30
- 31. 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 31
- 32. The Object Oriented Model (continued) 32
- 33. 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 33
- 34. 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 34
- 35. Data Models: A Summary (continued) 35
- 36. 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 36
- 37. 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 37
- 38. Degrees of Data Abstraction (continued) 38
- 39. 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 39
- 40. The 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 40
- 41. The External Model (continued) 41
- 42. 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 42
- 43. The Conceptual Model (continued) 43
- 44. 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 44
- 45. The 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 45
- 46. The Internal Model (continued) 46
- 47. 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 47
- 48. The Physical Model (continued) 48
- 49. Summary A data model is a (relatively) simple abstraction of a complex real- world data environment Basic data modeling components are: Entities Attributes Relationships Constraints 49
- 50. 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 50
- 51. Summary (continued) Object is basic modeling structure of object oriented data model The relational model has adopted many object-oriented 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 51