Importance of Data Models in Business

1. DB2 ITS232Introduction To Database Oracle MySQL MS Access FILE SYSTEMS & DATABASE 1.5: Data Models Chapter 1

2. Data ModelsThe 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

3. Data Models Data Model Basic Building Blocks

4. Data Models Data Model Basic Building Blocks • Based on previous IBM DB2 lab, determine:

5. Data Models 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

6. Data Models Business Rules • Must be rendered in writing/available in written form • Must be kept up to date • Sometimes are external to the organization • Must be easy to understand and widely distributed • Describe characteristics of the data as viewed by the company:

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

8. Data Models Discovering Business Rules • Business rules example:

9. Data Models Translating Business Rules into Data Model Components • Standardize company’s view of data • Act as a communications tool between users and designers • Allow designer: • to understand the nature, role, and scope of data • to understand business processes • to develop appropriate relationship participation rules and constraints • Promote creation of an accurate data model

10. Data Models 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.

11. Data Models Fact Finding Techniques 5 commonly used fact finding techniques

12. Data Models The Evolution of Data Models

13. Hierachical Database Model Data Models 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 • 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

14. Hierachical Database Model Data Models The Evolution of Data Models

15. Abu Johor 3000 Samad Kedah 2500 Zaitun Melaka 4500 • Hierachical Database Model Data Models The Evolution of Data Models Root A001 A002 A003 A004 Nut Washer Washer Hammer Nut Bolt Nut

16. Hierachical Database Model Data Models The Evolution of Data Models Root Segment Source: http://worldacademyonline.com/article/25/359/data_models__relational__hierarchical_and_network_.html

17. Hierachical Database Model Data Models The Evolution of Data Models • 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

18. Data Models 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 • Resembles hierarchical model • Collection of records in 1:M relationships

19. Data Models The Evolution of Data Models • Network Database Model • 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 • A parent can have many child records • A child can have more than one parent record

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

21. Abu Johor 3000 Samad Kedah 2500 Zaitun Melaka 4500 Data Models The Evolution of Data Models • Network Database Model CUSTOMER INVOICE PRODUCT A001 Nut Washer A002 A003 Hammer A004 Bolt

22. Data Models The Evolution of Data Models • Network Database Model Source: http://worldacademyonline.com/article/25/359/data_models__relational__hierarchical_and_network_.html

23. Data Models The Evolution of Data Models • Network Database Model • Disadvantages • Too cumbersome/difficult to handle • 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

24. Data Models 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 • 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

25. Data Models The Evolution of Data Models • Relational Model

26. Data Models The Evolution of Data Models • Relational Model • Example of table structure/relational table

27. Data Models The Evolution of Data Models • Relational Model • Example of table with data/relational table

28. Data Models The Evolution of Data Models • Relational Model • Example of table relationship/relational diagram

29. Data ModelsThe Evolution of Data Models • Relational Model • Example of form

30. Data ModelsThe 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

31. Data ModelsThe 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

32. Data ModelsThe Evolution of Data Models • Relational Model • Example of ERD Chen Crow’s Foot

33. Data ModelsThe 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)

34. Data ModelsThe Evolution of Data Models • Object Oriented Model • Object described by its factual content equivalent to entity in Relational Model • Includes information about relationships between facts within object, and relationships with other objects but still unlike relational model’s entity • Subsequent OODM development allowed an object to also contain all operations: changing its data values, finding specific data values, printing data values • Object becomes basic building block for autonomous structures

35. Data Models The Evolution of Data Models • Object Oriented Model • Object is an abstraction of a real-world entity • E.g. PERSON, VEHICLE • Attributes describe the properties of an object • E.g. Name, IC Number, Address • Objects that share similar characteristics are grouped in classes • Shared structured (attributes) and behavior (methods) • 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

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

37. 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

38. Data ModelsA Summary: The development of data model Semantic data - data is organized in such a way that it can be interpreted meaningfully without human intervention

39. Data ModelsDegrees 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 • The major purpose of a database system is to provide users with an abstract view of the system. • The system hides certain details of how data is stored and created and maintained • Complexity should be hidden from database users.

40. Data ModelsDegrees of Data Abstraction • American National Standards Institute (ANSI) Standards Planning and Requirements Committee (SPARC) • Defined a framework for data modeling based on degrees of data abstraction (1970s): • The famous “Three Level ANSI-SPARC Architecture”

41. Data Models Degrees of Data Abstraction • Data abstraction levels

42. Data ModelsThree Level ANSI-SPARC Architecture User 2 User 1 User n -user’s view External Model … View 1 View 2 View n 1. External level ERD -designer’s view -h/w independent -s/w independent 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

43. Three Level ANSI-SPARC ArchitectureExternal 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

44. Three Level ANSI-SPARC ArchitectureExternal Model • Example of External Model for Tiny College

45. Three Level ANSI-SPARC Architecture 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 • Basis for identification and high-level description of main data objects, avoiding details • 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

46. Three Level ANSI-SPARC Architecture Conceptual Model • Example of Conceptual Model for Tiny college

47. Three Level ANSI-SPARC Architecture 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

48. Three Level ANSI-SPARC Architecture Internal Model • An Internal Model for Tiny College

49. Three Level ANSI-SPARC Architecture Physical 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

50. Three Level ANSI-SPARC Architecture Physical Model The Physical Model