Chapter 1: The Database Environment

1. Chapter 1:The Database Environment © Prentice Hall

2. Objectives • Definition of terms • Explain growth and importance of databases • Name limitations of conventional file processing • Identify five categories of databases • Explain advantages of databases • Identify costs and risks of databases • List components of database environment • Describe evolution of database systems

3. Definitions • Data: stored representations of meaningful objects and events • Structured: numbers, text, dates • Unstructured: images, video, documents • Database: organized collection of logically related data • Information: data processed to increase knowledge in the person using the data • Metadata: data that describes the properties and context of user data

4. Systems and Procedures • Product flow and information flow • Product flow: the flow of raw materials into assemblies and finally into finished goods. • Information flow: the creation of movement of the administrative and operational documentation necessary for product flow.

5. Product flow and Information flow Customers Employees Collection (8) Billing (7) Paying (9) Vendors Purchasing (1) Sales (5) Production (4) Distribution (6) Inventory (3) Receiving (2) • Data to Information flow is as if raw material to production flow.

6. The evolution of product flow and information flow ? raw materials production finished goods technical support Input Output ? data processing information further processing

7. Figure 1-1a Data in context Context helps users understand data

8. Figure 1-1b Summarized data Graphical displays turn data into useful information that managers can use for decision making and interpretation

9. Descriptions of the properties or characteristics of the data, including data types, field sizes, allowable values, and data context

10. Disadvantages of File Processing • Program-Data Dependence • All programs maintain metadata for each file they use • Duplication of Data • Different systems/programs have separate copies of the same data • Limited Data Sharing • No centralized control of data • Lengthy Development Times • Programmers must design their own file formats • Excessive Program Maintenance • 80% of information systems budget

11. Duplicate Data Figure 1-3 Old file processing systems at Pine Valley Furniture Company

12. Problems with Program Data Dependency • Each application programmer must maintain his/her own data • Each application program needs to include code for the metadata of each file • Each application program must have its own processing routines for database operations: reading, inserting, updating, and deleting data • Lack of coordination and central control • Non-standard file formats

13. Problems with Data Redundancy • Waste of space to have duplicate data • Causes more maintenance headaches • The biggest problem: • Data changes in one file could cause inconsistencies • Compromises in data integrity

14. SOLUTION: The DATABASE Approach • Central repository of shared data • Data is managed by a controlling agent • Stored in a standardized, convenient form Requires a Database Management System (DBMS)

15. Database Management System • A software system that is used to create, maintain, and provide controlled access to user databases Order Filing System Central database Contains employee, order, inventory, pricing, and customer data Invoicing System DBMS Payroll System DBMS manages data resources like an operating system manages hardware resources

16. Advantages of the Database Approach • Program-data independence (PI) • Planned (minimal) data redundancy (DR) • Improved data consistency • Improved data sharing • Increased application development productivity • Enforcement of standards • Improved data quality • Improved data accessibility and responsiveness • Reduced program maintenance • Improved decision support

17. Costs and Risks of the Database Approach • New, specialized personnel • Installation and management cost and complexity • Conversion costs • Need for explicit backup and recovery • Organizational conflict

18. Figure 2-9 Three-tiered client/server database architecture Presentation tier Business logic tier Data tier

19. Evolution of DB Systems

20. Chapter 2: The Database Development Process © Prentice Hall

21. Objectives • Definition of terms • Describe system development life cycle • Explain prototyping approach • Explain roles of individuals • Explain three-schema approach • Explain role of packaged data models • Explain three-tiered architectures • Explain scope of database design projects • Draw simple data models

22. Information Systems Architecture(ISA) • Conceptual blueprint for organization’s desired information systems structure • Consists of (6Ws): • Processes–data flow diagrams, process decomposition, etc. (DFD- Data Flow Diagram) • Data (e.g. Enterprise Data Model–ER Diagram) • Data Network–topology diagram (like Fig 1-9) • People–people management using project management tools (Gantt charts, etc.) • Events and points in time (when processes are performed. Use case diagram) • Reasons for events and rules (e.g., decision tables) How What Where Who When Why

23. Data Flow Diagrams • Data flow diagrams (DFDs) are graphical aids that describe an information system • Advantages: • freedom from committing to the technical implementation of the system too early. • Further understanding the interrelatedness of systems and subsystems. • communicating current system knowledge to users .

24. Data Flow Diagrams • Data flow diagram symbols • Four basic symbols • Process • Data flow • Data store • External entity

25. Process External entities Data flows

26. Data store

29. Figure 2-2 Example of process decomposition of an order fulfillment function (Pine Valley Furniture) Decomposition = breaking large tasks into smaller tasks in a hierarchical structure chart Order form Credit status

30. Information Systems Architecture(ISA) • Conceptual blueprint for organization’s desired information systems structure • Consists of (6Ws): • Processes–data flow diagrams, process decomposition, etc. (DFD- Data Flow Diagram) • Data (e.g. Enterprise Data Model–ER Diagram) • Data Network–topology diagram (like Fig 1-9) • People–people management using project management tools (Gantt charts, etc.) • Events and points in time (when processes are performed. Use case diagram) • Reasons for events and rules (e.g., decision tables) How What Where Who When Why

31. Develop Enterprise Model • Process: Data flow diagram (DFD) Functional decomposition Iterative process breaking system description into finer and finer detail • Data: Entity Relationship diagram (ER Diagram) • Planning matrixes Describe interrelationships between planning objects

32. Data Dictionary Data Element (field) Data Entity (table) Data Entity (table) Data Modeling using ERD Program Modules

33. Spot missing entity Example business function-to-data entity matrix (Fig. 2-3) Higher priority

34. Planning Matrixes • Describe relationships between planning objects in the organization • Types of matrixes: • Function-to-data entity • Location-to-function • Unit-to-function • IS-to-data entity • Supporting function-to-data entity • IS-to-business objective

35. Database Schema • Conceptual Schema • E-R models–covered in Chapters 3 and 4 • External Schema • User Views: schema for different users • Subsets of Conceptual Schema • Can be determined from business-function/data entity matrices • Physical Schema (Internal Schema) • Physical structures–covered in Chapters 5 and 6

36. Figure 2-7 Three-schema architecture Different people have different views of the database…these are the external schema The internal schema is the underlying design and implementation

37. Figure 2-8 Developing the three-tiered architecture

38. Information Engineering • Top-down planning–a generic IS planning methodology for obtaining a broad understanding of the IS needed by the entire organization • Four steps to Top-Down planning: • Planning • Analysis • Design • Implementation

39. Two Approaches to Database and IS Development • SDLC • System Development Life Cycle • Detailed, well-planned development process • Time-consuming, but comprehensive • Long development cycle • Prototyping • Rapid application development (RAD) • Cursory attempt at conceptual data modeling • Define database during development of initial prototype • Repeat implementation and maintenance activities with new prototype versions

40. Planning Analysis Logical Design Physical Design Implementation Maintenance Systems Development Life Cycle(see also Figures 2.4, 2.5)

41. Systems Development Life Cycle • Systems planning • Purpose – identify problem’s nature/scope • Systems request – begins the process & describes desired changes/improvements • Systems planning – includes preliminary investigation or feasibility study • End product – preliminary investigation report

42. Systems Development Life Cycle • Systems analysis • Purpose is to learn exactly how the current system operates or determine what systems should do. • Fact-finding or requirements determination is used to define all functions of the current system

43. Systems Development Life Cycle • Options • Develop a system in-house • Purchase a commercial package • Modify an existing system • Stop development • The end product for this phase is the systems requirements document

44. Systems Development Life Cycle • Systems design • Purpose is to satisfy all documented requirements • identify whatandhowthe system must do. • Identify all outputs, inputs, files, manual procedures, & application programs • user interface design, files organization and database design • Avoid misunderstanding through manager and user involvement • End product is system design specification

45. Systems Development Life Cycle • Systems implementation • Construct/deliver information system • Prepares functioning, documented system • Write, test, document application programs • User and manager approval obtained • File conversion occurs • Users, managers, IS staff trained to operate and support the system • Post-implementation evaluation performed

46. Prototyping Database Methodology(Figure 2.6)

47. Prototyping Database Methodology(Figure 2.6) (cont.)

48. Prototyping Database Methodology(Figure 2.6) (cont.)

49. Prototyping Database Methodology(Figure 2.6) (cont.)

50. Prototyping Database Methodology(Figure 2.6) (cont.)