Chapter 1: The Database Environment

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1 Chapter 1: The Database Environment Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden Prentice Hall,

2 Definitions Data: Meaningful facts, text, graphics, images, sound, video segments Database: An organized collection of logically related data Information: Data processed to be useful in decision making Metadata: Data that describes data Chapter 1 2 Prentice Hall, 2002

3 Figure 1-1a Data in Context Large volume of facts which are difficult to interpret Chapter 1 3 Prentice Hall, 2002

4 Figure 1-1b Summarized data Useful information that managers can use for decision making and interpretation Chapter 1 4 Prentice Hall, 2002

5 Table 1-1 Metadata Descriptions of the properties or characteristics of the data, including data types, field sizes, allowable values, and documentation Chapter 1 5 Prentice Hall, 2002

6 Databases vs. File Processing Data is stored in two electronic forms: Databases A central data-store that can be read from different sources Program Files Distributed data-stores that can usually only be read from a single source Common Examples: Word, Excel, PowerPoint Chapter 1 6 Prentice Hall, 2002

7 Disadvantages of File Processing Program-Data Dependence All programs maintain metadata for each file they use Data Redundancy (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 Chapter 1 7 Prentice Hall, 2002

8 Figure 1-2 Three file processing systems at Pine Valley Furniture Duplicate Data Chapter 1 8 Prentice Hall, 2002

9 Problems with Data Dependency Each application programmer must maintain their 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 reading, inserting, updating and deleting data Lack of coordination and central control Non-standard file formats Chapter 1 9 Prentice Hall, 2002

10 Problems with Data Duplication Waste of space to have duplicate data Causes more maintenance headaches The biggest Problem: When data changes in one file, could cause inconsistencies Compromises data integrity Chapter 1 10 Prentice Hall, 2002

11 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) Chapter 1 11 Prentice Hall, 2002

12 Database Management System A DBMS is a data storage and retrieval system which permits data to be stored nonredundantly while making it appear to the user as if the data is well-integrated. Chapter 1 12 Prentice Hall, 2002

13 Database Management System Application #1 Application #2 DBMS Database containing centralized shared data Application #3 DBMS manages data resources like an operating system manages hardware resources Chapter 1 13 Prentice Hall, 2002

14 Advantages of Database Approach Program-Data Independence Metadata stored in DBMS, so applications don t need to worry about data formats Data queries/updates managed by DBMS so programs don t need to process data access routines Results in: increased application development and maintenance productivity Minimal Data Redundancy Leads to increased data integrity/consistency Chapter 1 14 Prentice Hall, 2002

15 Advantages of Database Approach Improved Data Sharing Different users get different views of the data Enforcement of Standards All data access is done in the same way Improved Data Quality Constraints, data validation rules Better Data Accessibility/ Responsiveness Use of standard data query language (SQL) Security, Backup/Recovery, Concurrency Disaster recovery is easier Chapter 1 15 Prentice Hall, 2002

16 Costs and Risks of the Database Approach Up-front costs: Installation Management Cost and Complexity Conversion Costs Ongoing Costs Requires New, Specialized Personnel Need for Explicit Backup and Recovery Organizational Conflict Old habits die hard Chapter 1 16 Prentice Hall, 2002

17 The Enterprise Data Model A graphical model that shows the high-level entities for the organization and the relationships among those entities Chapter 1 17 Prentice Hall, 2002

18 Figure 1-3 Segment from enterprise data model Figure 3 Chapter 1 18 Prentice Hall, 2002

19 Figure 1-3 Segment from enterprise data model Figure 3 Business Rule: One customer may place many orders, but each order is placed by a single customer One-to-many relationship Chapter 1 19 Prentice Hall, 2002

20 Figure 1-3 Segment from enterprise data model Figure 3 Business Rule: One order has many order lines; each order line is associated with a single order One-to-many relationship Chapter 1 20 Prentice Hall, 2002

21 Figure 1-3 Segment from enterprise data model Figure 3 Business Rule: One product can be in many order lines, each order line refers to a single product One-to-many relationship Chapter 1 21 Prentice Hall, 2002

22 Figure 1-3 Segment from enterprise data model Figure 3 Therefore, one order involves many products and one product is involved in many orders Many-to-many relationship Chapter 1 22 Prentice Hall, 2002

23 Implementing Relational Databases How Many Records Are Involved: More records require a more powerful system Example: >500K Records shouldn t use Access How Many People Will Use the RDMS More DB connections require more power Example: >25 People shouldn t use Access What Structure Currently Exists Using existing systems can keep costs down Chapter 1 23 Prentice Hall, 2002

24 Figure 1-4 Pine Valley Furniture Company Access DB Order, Order_Line, Customer, and Product tables Relationships established in special columns that provide links between tables Chapter 1 24 Prentice Hall, 2002

25 Figure 1-5 Client/server system for Pine Valley Furniture Company Is having the database server and database enough? Chapter 1 25 Prentice Hall, 2002

26 The Database Application Create: Add new data to the database Read: Read current database data Update: Modify current database data Delete: Remove current data from the database Chapter 1 26 Prentice Hall, 2002

27 Figure 1-6 Customer invoice (Pine Valley Furniture Company) Application program functions: inserting new data, updating existing data, deleting existing data, reading data for display Chapter 1 27 Prentice Hall, 2002

28 The Range of Database Applications Personal Database standalone desktop database Workgroup Database local area network (<25 users) Department Database local area network ( users) Enterprise Database wide-area network (hundreds or thousands of users) Chapter 1 28 Prentice Hall, 2002

29 Figure 1-7 Typical data from a personal computer database Chapter 1 29 Prentice Hall, 2002

30 Figure 1-8 Workgroup database with local area network Chapter 1 30 Prentice Hall, 2002

31 Figure 1-9 An enterprise data warehouse Chapter 1 31 Prentice Hall, 2002

32 Components of the Database Environment CASE Tools computer-aided software engineering Repository centralized storehouse of metadata Database Management System (DBMS) software for managing the database Database storehouse of the data Application Programs software using the data User Interface text and graphical displays to users Data Administrators personnel responsible for maintaining the database System Developers personnel responsible for designing databases and software End Users people who use the applications and databases Chapter 1 32 Prentice Hall, 2002

33 Evolution of DB Systems Flat files s s Hierarchical 1970s s Network 1970s s Relational 1980s - present Object-oriented 1990s - present Object-relational 1990s - present Data warehousing 1980s - present Web-enabled 1990s - present Chapter 1 33 Prentice Hall, 2002

34 Questions \ Discussion Why might a firm choose to implement a database system? What is the Enterprise Data Model (no it is not a small plastic starship)? What are two benefits of the Relational Database Approach? Chapter 1 34 Prentice Hall, 2002

35 Chapter 2: The Database Development Process Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden 35

36 Information Systems Architecture (ISA) Overall blueprint for organization s information systems Consists of: Data (Enterprise Data Model simplified ER Diagram) Processes data flow diagrams, process decomposition, etc. Data Network topology diagram (like fig 1.8) People people management using project management tools (Gantt charts, etc.) Events and Points in Time (when processes are performed) Reasons for events and rules (e.g. decision tables) Chapter

37 Information Engineering A data-oriented methodology to create and maintain information systems Top-down planning approach. Four steps: Planning Results in an Information Systems Architecture Analysis Results in functional specifications i.e. what we want Design Results in design specifications i.e. how we ll do it Implementation Results in final operational system Chapter

38 Information Systems Planning Strategy development IT Planning to meet Corporate strategy Three steps: 1. Identify strategic planning factors 2. Identify corporate planning objects 3. Develop enterprise model Chapter

39 Identify Strategic Planning Factors (table 2.1) Organization goals what we hope to accomplish Critical success factors what MUST work in order for us to survive Problem areas weaknesses we now have Chapter

40 Identify Corporate Planning Objects (table 2.3) Organizational units Organizational locations Business functions these might become the users Entity types the things we are trying to model Information (application) systems Chapter

41 Develop Enterprise Model Decomposition of business functions See figure 2.2 Enterprise data model See figure 2.1 Planning matrixes See figure 2.3 Chapter

42 Enterprise Data Model First step in database development Specifies scope and general content Overall picture of organizational data, not specific design Entity-relationship diagram Descriptions of entity types Relationships between entities Business rules Chapter

43 Figure 2-1 Segment from enterprise data model (Pine Valley Furniture Company) [simplified E-R diagram, repeat of figure 1.3] Enterprise data model describes the entities in an organization and the relationship between these entities Chapter

44 Figure Example of process decomposition of an order fulfillment function (Pine Valley Furniture) Decomposition -- breaking large tasks into smaller tasks in a hierarchical structure chart Chapter

45 Planning Matrixes Function-to-data entity Location-to-function Unit-to-function IS-to-data entity Supporting function-to-data entity which data are captured, used, updated, deleted within each function IS-to-business objective Chapter

46 Customer Product Raw Material Order Work Center Work Order Invoice Equipment Employee Example business function-todata entity matrix (fig. 2.3) Data Entity Types Business Function (users) Business Planning X X X X Product Development X X X X Materials Management X X X X X X Order Fulfillment X X X X X X X X X Order Shipment X X X X X X Sales Summarization X X X X X Production Operations X X X X X X X Finance and Accounting X X X X X X X X Chapter

47 Alternative 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. Chapter

48 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Analysis Logical Design Physical Design Implementation Maintenance Chapter

49 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose --preliminary understanding Deliverable request for project Analysis Logical Design Physical Design Database activity enterprise modeling Implementation Maintenance Chapter

50 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose state business situation and solution Deliverable request for analysis Analysis Logical Design Physical Design Database activity conceptual data modeling Implementation Maintenance Chapter

51 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose thorough analysis Deliverable functional system specifications Analysis Logical Design Physical Design Database activity conceptual data modeling Implementation Maintenance Chapter

52 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose information requirements structure Deliverable detailed design specifications Analysis Logical Design Physical Design Database activity logical database design Implementation Maintenance Chapter

53 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose develop technology specs Deliverable program/data structures, technology purchases, organization redesigns Analysis Logical Design Physical Design Database activity physical database design Implementation Maintenance Chapter

54 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose programming, testing, training, installation, documenting Deliverable operational programs, documentation, training materials Analysis Logical Design Physical Design Database activity database implementation Implementation Maintenance Chapter

55 Systems Development Life Cycle (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose monitor, repair, enhance Deliverable periodic audits Analysis Logical Design Physical Design Database activity database maintenance Implementation Maintenance Chapter

56 Figure 2-6 The prototyping methodology and database development process Chapter

57 Figure 2-6 The prototyping methodology and database development process Chapter

58 Figure 2-6 The prototyping methodology and database development process Chapter

59 Figure 2-6 The prototyping methodology and database development process Chapter

60 Figure 2-6 The prototyping methodology and database development process Chapter

61 Managing Projects: People Involved Systems analysts Database analysts Users Programmers Database/data administrators Systems programmers, network administrators, testers, technical writers Chapter

62 Figure 2-7a Gantt Chart Shows time estimates of tasks Chapter

63 Figure 2-7b PERT chart Shows dependencies between tasks Chapter

64 Database Schema Physical Schema Physical structures covered in chapters 5 and 6 Conceptual Schema ER models covered in chapters 3 and 4 External Schema User Views Subsets of Conceptual Schema Can be determined from business-function/data entity matrices DBA determines schema for different users This is part of people-management in databases Chapter

65 Figure 2-8 Three-schema database architecture External schema Different people have different views of the database these are the external schema Internal schema Chapter

66 Figure 2-10 Three-tiered client/server database architecture Chapter

67 Pine Valley Furniture Preliminary data model (figure 2-11) Chapter

68 Chapter 3: Modeling Data in the Organization Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden Prentice Hall,

69 SDLC Revisited Data Modeling is an Analysis Activity (figures 2.4, 2.5) Project Identification and Selection Project Initiation and Planning Purpose thorough analysis Deliverable functional system specifications Analysis Logical Design Physical Design Database activity conceptual data modeling Implementation Maintenance Chapter 1 Prentice Hall,

70 Business Rules Statements that define or constrain some aspect of the business Assert business structure Control/influence business behavior Expressed in terms familiar to end users Automated through DBMS software Chapter 1 Prentice Hall,

71 A Good Business Rule is: Declarative what, not how Precise clear, agreed-upon meaning Atomic one statement Consistent internally and externally Expressible structured, natural language Distinct non-redundant Business-oriented understood by business people Chapter 1 Prentice Hall,

72 E-R Model Constructs Entity instance - person, place, object, event, concept (often corresponds to a row in a table) Entity Type collection of entities (often corresponds to a table) Attribute - property or characteristic of an entity type (often corresponds to a field in a table) Relationship instance link between entities (corresponds to primary key-foreign key equivalencies in related tables) Relationship type category of relationship link between entity types Chapter 1 Prentice Hall,

73 Sample E-R Diagram (figure 3-1) Chapter 1 Prentice Hall,

74 Figure Basic E-R Notation Entity symbols A special entity that is also a relationship Relationship symbols Attribute symbols Chapter 1 Prentice Hall,

75 What Should an Entity Be? SHOULD BE: An object that will have many instances in the database An object that will be composed of multiple attributes An object that we are trying to model SHOULD NOT BE: A user of the database system An output of the database system (e.g. a report) Chapter 1 Prentice Hall,

76 Figure 3-4 Inappropriate entities System user System output Appropriate entities Chapter 1 Prentice Hall,

77 Attributes Attribute - property or characteristic of an entity type Classifications of attributes: Simple versus Composite Attribute Single-Valued versus Multivalued Attribute Stored versus Derived Attributes Identifier Attributes Chapter 1 Prentice Hall,

78 Identifiers (Keys) Identifier (Key) - An attribute (or combination of attributes) that uniquely identifies individual instances of an entity type Simple Key versus Composite Key Candidate Key an attribute that could be a key satisfies the requirements for being a key Chapter 1 Prentice Hall,

79 Characteristics of Identifiers Will not change in value Will not be null No intelligent identifiers (e.g. containing locations or people that might change) Substitute new, simple keys for long, composite keys Chapter 1 Prentice Hall,

80 Figure A composite attribute An attribute broken into component parts Chapter 1 Prentice Hall,

81 Figure 3-9a Simple key attribute The key is underlined Chapter 1 Prentice Hall,

82 Figure 3-9b -- Composite key attribute The key is composed of two subparts Chapter 1 Prentice Hall,

83 Figure Entity with a multivalued attribute (Skill) and derived attribute (Years_Employed) What s wrong with this? Derived from date employed and current date Multivalued: an employee can have more than one skill Chapter 1 Prentice Hall,

84 Figure 3-19 an attribute that is both multivalued and composite This is an example of time-stamping Chapter 1 Prentice Hall,

85 More on Relationships Relationship Types vs. Relationship Instances The relationship type is modeled as the diamond and lines between entity types the instance is between specific entity instances Relationships can have attributes These describe features pertaining to the association between the entities in the relationship Two entities can have more than one type of relationship between them (multiple relationships) Associative Entity = combination of relationship and entity More on this later Chapter 1 Prentice Hall,

86 Degree of Relationships Degree of a Relationship is the number of entity types that participate in it Unary Relationship Binary Relationship Ternary Relationship Chapter 1 Prentice Hall,

87 Degree of relationships from figure 3-2 One entity related to another of the same entity type Entities of two different types related to each other Entities of three different types related to each other Chapter 1 Prentice Hall,

88 Cardinality of Relationships One to One Each entity in the relationship will have exactly one related entity One to Many An entity on one side of the relationship can have many related entities, but an entity on the other side will have a maximum of one related entity Many to Many Entities on both sides of the relationship can have many related entities on the other side Chapter 1 Prentice Hall,

89 Cardinality Constraints Cardinality Constraints - the number of instances of one entity that can or must be associated with each instance of another entity. Minimum Cardinality If zero, then optional If one or more, then mandatory Maximum Cardinality The maximum number Chapter 1 Prentice Hall,

90 Cardinality figure 3-2 Chapter 1 Prentice Hall,

91 Unary relationships -- figure 3-12a Chapter 1 Prentice Hall,

92 Binary relationships figure 3-12b Chapter 1 Prentice Hall,

93 Ternary relationships figure 3-12c Note: a relationship can have attributes of its own Chapter 1 Prentice Hall,

94 Basic relationship with only maximum cardinalities showing figure 3-16a Mandatory minimum cardinalities figure 3-17a Chapter 1 Prentice Hall,

95 Figure 3-17c Optional cardinalities with unary degree, one-to-one relationship Chapter 1 Prentice Hall,

96 Figure 3-10a Relationship type 3-10b Entity and Relationship instances Chapter 1 Prentice Hall,

97 Figure 3-11a A binary relationship with an attribute Here, the date completed attribute pertains specifically to the employee s completion of a course it is an attribute of the relationship Chapter 1 Prentice Hall,

98 Figure 3-12c -- A ternary relationship with attributes Chapter 1 Prentice Hall,

99 Figure 3-13a A unary relationship with an attribute. This has a many-to-many relationship Representing a bill-of -materials structure Chapter 1 Prentice Hall,

100 Examples of multiple relationships entities can be related to one another in more than one way Figure 3-21a Employees and departments Chapter 1 Prentice Hall,

101 Figure 3-21b -- Professors and courses (fixed upon constraint) Here,max cardinality constraint is 4 Chapter 1 Prentice Hall,

102 Figure 3-15: Multivalued attribute vs. relationship. Alternative approaches Chapter 1 Prentice Hall,

103 Strong vs. Weak Entities, and Identifying Relationships Strong entities exist independently of other types of entities has its own unique identifier represented with single-line rectangle Weak entity dependent on a strong entity cannot exist on its own Does not have a unique identifier represented with double-line rectangle Identifying relationship links strong entities to weak entities represented with double line diamond Chapter 1 Prentice Hall,

104 Figure 3-5: Strong and weak entities Strong entity Identifying relationship Weak entity Chapter 1 Prentice Hall,

105 Associative Entities It s an entity it has attributes AND it s a relationship it links entities together When should a relationship with attributes instead be an associative entity? All relationships for the associative entity should be many The associative entity could have meaning independent of the other entities The associative entity preferably has a unique identifier, and should also have other attributes The associative may be participating in other relationships other than the entities of the associated relationship Ternary relationships should be converted to associative entities (p102) Chapter 1 Prentice Hall,

106 Figure 3-11b: An associative entity (CERTIFICATE) Associative entity involves a rectangle with a diamond inside. Note that the many-to-many cardinality symbols face toward the associative entity and not toward the other entities Chapter 1 Prentice Hall,

107 Figure 3-13c -- an associative entity bill of materials structure This could just be a relationship with attributes it s a judgment call Chapter 1 Prentice Hall,

108 Figure Ternary relationship as an associative entity Chapter 1 Prentice Hall,

109 Figure 3-22 E-R diagram for Pine Valley Furniture Chapter 1 Prentice Hall,

110 Chapter 4: The Enhanced E-R Model and Business Rules Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden Prentice Hall,

111 Supertypes and Subtypes Subtype: A subgrouping of the entities in an entity type which has attributes that are distinct from those in other subgroupings Supertype: An generic entity type that has a relationship with one or more subtypes Inheritance: Subtype entities inherit values of all attributes of the supertype An instance of a subtype is also an instance of the supertype Chapter Prentice Hall, 2002

112 Figure 4-1 Basic notation for supertype/subtype relationships Chapter Prentice Hall, 2002

113 Figure Employee supertype with three subtypes All employee subtypes will have emp nbr, name, address, and date-hired Each employee subtype will also have its own attributes Chapter Prentice Hall, 2002

114 Relationships and Subtypes Relationships at the supertype level indicate that all subtypes will participate in the relationship The instances of a subtype may participate in a relationship unique to that subtype. In this situation, the relationship is shown at the subtype level Chapter Prentice Hall, 2002

115 Figure Supertype/subtype relationships in a hospital Both outpatients and resident patients are cared for by a responsible physician Only resident patients are assigned to a bed Chapter Prentice Hall, 2002

116 Generalization and Specialization Generalization: The process of defining a more general entity type from a set of more specialized entity types. BOTTOM-UP Specialization: The process of defining one or more subtypes of the supertype, and forming supertype/subtype relationships. TOP-DOWN Chapter Prentice Hall, 2002

117 Figure 4-4 Example of generalization (a) Three entity types: CAR, TRUCK, and MOTORCYCLE All these types of vehicles have common attributes Chapter Prentice Hall, 2002

118 Figure 4-4(b) Generalization to VEHICLE supertype Note: no subtype for motorcycle, since it has no unique attributes Chapter Prentice Hall, 2002

119 Figure 4-5 Example of specialization (a) Entity type PART Applies only to purchased parts Only applies to manufactured parts Chapter Prentice Hall, 2002

120 Figure 4-5(b) Specialization to MANUFACTURED PART and PURCHASED PART Created 2 subtypes Note: multivalued attribute was replaced by a relationship to another entity Chapter Prentice Hall, 2002

121 Constraints in Supertype/ Completeness Constraint Completeness Constraints: Whether an instance of a supertype must also be a member of at least one subtype Total Specialization Rule: Yes (double line) Partial Specialization Rule: No (single line) Chapter Prentice Hall, 2002

122 Figure 4-6 Examples of completeness constraints (a) Total specialization rule A patient must be either an outpatient or a resident patient Chapter Prentice Hall, 2002

123 Figure 4-6(b) Partial specialization rule A vehicle could be a car, a truck, or neither Chapter Prentice Hall, 2002

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