ITS232 Introduction To Database Management Systems

1. ITS232Introduction To Database Management Systems SitiNurbaya Ismail Faculty of Computer Science & Mathematics, UniversitiTeknologi MARA (UiTM), Kedah | sitinurbaya@kedah.uitm.edu.my | http://www.sitinur151.wordpress.com | | A2-3039 | ext:2561 | 012-7760562 | CHAPTER 2 Data Models

2. Chapter 2: Data Models 2.0 DATA MODELS 2.1 The Importance of Data Models 2.2 Data Model Basic Building Blocks 2.3 Business Rules 2.4 The Evolution of Data Models 2.5 Degrees of Data Abstraction

3. 2.0 Data Models2.1 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 • End-users have different views and needs for data • Data model organizes data for various users

4. 2.0 Data Models2.2 Data Model Basic Building Blocks

5. 2.0 Data Models 2.2 Data Model Basic Building Blocks

6. 2.0 Data Models 2.3 Business Rules • Brief, precise, and unambiguous descriptions of 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

7. 2.0 Data Models 2.3 Business Rules • 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:

8. 2.0 Data Models 2.3 Business Rules: Discovering Business Rules • Sources of Business Rules • Company managers • Policy makers • Department managers • Written documentation • Procedures • Standards • Operations manuals • Direct interviews with end users

9. 2.0 Data Models 2.3 Business Rules: Business Rules Example • One student can register many subjects • A painter can paint many paintings; each paining is painted by one painter. A gallery can have many paintings. A painting can be exhibited by a gallery.

10. 2.0 Data Models 2.3 Business Rules: 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

11. 2.0 Data Models 2.3 Business Rules: Discovering Business Rules • Generally, nouns translate into entities • Verbs translate into relationships among entities • Relationships are bi-directional • Fact finding techniques: • The formal process of using techniques such as interview and questionnaire to collect facts about system, requirements and preferences. • To captures the essential facts necessary to build the required database • What facts are collected? • Captured facts about the current and/or future system

12. 2.0 Data Models 2.3 Business Rules: Fact Finding Techniques 5 commonly used fact finding techniques

13. 2.0 Data Models 2.4 The Evolution of Data Models

14. Hierachical Database Model 2.0 Data Models 2.4 The Evolution of Data Models • Developed in the 1960s to manage large amounts of data for complex manufacturing projects • Basic logical structure is represented by an upside-down “tree” or by a group of records that relates to each others by a pointer • The uppermost record is a Root/Parent • The lower record in a hierarchy is a Child • 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 • Can only support 1:1 and 1:M relationships where a child can only have one parent

15. Hierachical Database Model 2.0 Data Models 2.4 The Evolution of Data Models

16. 2.0 Data Models 2.4 The Evolution of Data Models Advantages of Hierachical Database Model Disadvantages of Hierachical Database Model Complex to implement Difficult to manage Lacks structural independence Implementation limitations Lack of standards • 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

17. 2.0 Data Models 2.4 The Evolution of Data Models • Network Database Model • Develop in 1970 in Conference on Data Systems Languages (CODASYL), by Database Task Group (DBTG) • Created to: • Represent complex data relationships more effectively • Improve database performance • Impose a database standard • Based on set theory, where a set consists of a collection of records

18. 2.0 Data Models 2.4 The Evolution of Data Models • Network Database Model • Resembles hierarchical model • Collection of records in 1:M relationships • Composed of at least two record types • Owner • Equivalent to the hierarchical model’s parent • Member • Equivalent to the hierarchical model’s child • Can support 1:1, 1:M and M:N relationships where a child can have more than one parent.

19. 2.0 Data Models 2.4 The Evolution of Data Models • Network Database Model

20. 2.0 Data Models 2.4 The Evolution of Data Models • Network Database Model • 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

21. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model • Developed by Codd (IBM) in 1970, considered ingenious but impractical in 1970 • Conceptually simple, based on mathematical concept of relational • Backwards, computers lacked power to implement the relational model • Today, microcomputers can run sophisticated relational database software • 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

22. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model

23. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model Example of table structure/relational table

24. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model Example of table with data/relational table

25. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model Relationship: An employee has one department. One department has many employee. EMPLOYEE DEPARTMENT has M 1 Example of table relationship/relational diagram

26. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model • 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

27. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model Entity Relationship (E-R) Model • Introduced by Chen in 1976 • Widely accepted and adapted graphical tool for data modeling • Graphical representation of entities and their relationships in dB structure • Entity Relationship Diagram (ERD) • Uses graphic representations to model database components • Entity is mapped to a relational table

28. 2.0 Data Models 2.4 The Evolution of Data Models • Relational Model Example of ERD

29. 2.0 Data Models 2.4 The Evolution of Data Models • Object Oriented Model • Modeled both data and their relationships in a single structure known as an object • OO data model (OODM) is the basis for the OO database management system (OODBMS)

30. 2.0 Data Models 2.4 The Evolution of Data Models • Object Oriented Model • 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

31. 2.0 Data Models 2.4 The Evolution of Data Models • Object Oriented Model • 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

32. 2.0 Data Models 2.4 The Evolution of Data Models • Object Oriented Model A comparison of the OO model and the ER model

33. 2.0 Data ModelsA 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. 2.0 Data ModelsA Summary

35. 2.0 Data Models2.5 Degrees of Data Abstraction • Way of classifying data models • Data abstraction: • hides extraneous information regarding data storage away from database user • Many processes begin at high level of abstraction and proceed to an ever-increasing level of detail

36. 2.0 Data Models2.5 Degrees of Data Abstraction • American National Standards Institute (ANSI) Standards Planning and Requirements Committee (SPARC) • Three Level ANSI-SPARC Architecture • Defined a framework for data modeling based on degrees of data abstraction(1970s):

37. 2.0 Data Models2.5 Degrees of Data Abstraction: Data Abstraction Levels

38. 2.0 Data Models2.5 Degrees of Data Abstraction • Three Level ANSI-SPARC Architecture User 2 User 1 User n -user’s view External Model … View 1 View 2 View n 1. External level -designer’s view -h/w independent -s/w independent ERD Conceptual Model Conceptual Schema 2. Conceptual level -DBMS’s view -h/w independent -s/w dependent Internal Schema Internal Model 3. Internal level Database -h/w dependent -s/w dependent Physical Model Physical data organization

39. 2.0 Data Models2.5 Degrees of Data Abstraction: 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 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. 2.0 Data Models2.5 Degrees of Data Abstraction: Conceptual Model • Global view of the entire database  concept of the dB • Describe what data is stored in the dB and relations among the data • Data as viewed by the entire organization  logical structure  communication tool between users and designer • Basis for identification and high-level description of main data objects, avoiding details  use as basic database bluprint • Most widely used conceptual model is the entity relationship (ER) model • Provides a relatively easily understood macro level view of data environment • Software and Hardware Independent • 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

41. 2.0 Data Models2.5 Degrees of Data Abstraction: Internal Model • Representation of the database as “seen” by the DBMS • Describes how the data is stored in the dB • Maps the conceptual model to the DBMS • Internal schema depicts a specific representation of an internal model • Physical representation of the dB on the computer • Software Dependent and Hardware Independent • Depend on the DBMS software used to implement the model • Does not depend on the hardware used in the implementation of the model

42. 2.0 Data Models2.5 Degrees of Data Abstraction: Internal Model The Physical Model • Operates at lowest level of abstraction, describing the way data are saved on storage media such as disks or tapes • how the data is stored in the database • Software and Hardware Dependent • Requires that database designers have a detailed knowledge of the hardware and software used to implement database design

43. 2.0 Data ModelsSummary • A data model is a (relatively) simple abstraction of a complex real-world data environment • Basic data modeling components are: • Entities • Attributes • Relationships • Constraints • Data modeling requirements are a function of different data views (global vs. local) and level of data abstraction

44. 2.0 Data Models Summary

45. Exercise 1 It is important to understand business rules when designing a database. • Define business rules. (1 marks)  • State TWO (2) reasons the importance of business rules in database design. (2 marks) • Give ONE (1) example of business rules. (1 marks)

46. Exercise 2 Briefly explain the hierarchical and network database model, and what is the difference between those two database models? (6 marks)

47. Exercise 3 What is the different between external, conceptual and internal model? (7 marks)

48. Exercise 4 What is a conceptual model and why it is important in database design? (4 marks)