Analyzing the Problem Using Data Modeling Methods

1. Analyzing the Problem Using Data Modeling Methods

3. Information: Embodies an expansion of knowledge perceived from data objects examined in their proper context.

4. Object: Something identifiable in the real- world that is independent of other objects and contains some property or a set of properties that uniquely identifies it.

5. What Users Need to Knowabout Business Objects: What are they? What are their characteristics are? How they might affect their job functions? How are they related to other business objects?

6. User Requirements: Collectively comprise the facts about business objects that the user wants know about and have manipulated by the information system.

7. Business Object Characteristics as User Information Needs: A Users information need can frequently be satisfied by just making them aware of various characteristics (properties or attributes) of business objects.

8. Business Object: Those objects internal or external to the business that comprise persons (or organizations), places, things, events, and concepts about which it is important to retain data.

9. Business Object Categories: Object Type Example________________ Person An administrator, teacher, or student Place The location of agencies or departments Thing A building, machine, or production item Event Enrolling in a seminar or college course Abstract A belief about something concept

11. Data Model: An abstract model about real-world data objects that reasonably symbolizes them.

12. Data Type (Object Type): Defines data objects together with their important properties and helps define the operations permitted on those objects and properties.

14. Data Model: Consists of a collection of data types.

15. The Five Basic Premises of Good Business Modeling: 1. Data is the center of modern information systems supported by data type identification. 2. The types of business data objects do not change very much. 3. Business functions change more frequently than data objects, but still do not change much.

16. Business Modeling Basic Premises (Cont) 4. Business processes change much more frequently than functions, but often remain the same for long periods of time. 5. Information systems procedures (how specifications) that use data objects frequently change.

17. Abstraction: The ability to hide detail and concentrate on general, common properties of a set of data types.

18. Generalized Data Type: A higher-level data type on which the firm has to also retain data that consists of a group of individual data types.

19. Generalized Data Type Example: An example of a generalized data type is a DEPARTMENT data type about which it would be important to retain data, such as DEPARTMENT HEAD, ADDRESS, TELEPHONE, etc. The DEPARTMENT data type would encompass other data types, such as DEPARTMENT PERSONNEL, SUPPLIES, EQUIPMENT, etc.

20. Complete Data Model: The entire collection of individual and generalized data types by searching archive data, interviewing personnel, and performing a host of other data gathering activities. The more complex and greater the number of the data types, the more complex is the data model.

21. Basic Criteria for Creatingthe Data Model: Identify all data types (individual and generalized) important to the business. Identify the properties of those data types to satisfy organizational personnel information needs. Identify relationships between those data types and the cardinality of those relationships

22. Entities: Data types in the high-level overview are modeled as entities in an E/RD to symbolize the real-world data type.

24. Defining Entities: An Entity is normally defined by properties that correspond to the properties of the data type it represents.

25. Entity Type Description: Defines an entity by listing and describing its properties, those entity characteristics considered significant to understanding the entity and the sufficiently model the real-world data type.

26. Entity Property: A named attribute of an entity having a value that describes, characterizes, classifies, and identifies the characteristics of the data type that the entity symbolizes. An entity property associates properties of an entity to attribute values from a domain of possible values.

27. Domain: A uniquely named collection of permissible values for a given property. Domain Definition: A designation that limits the value for an entity property to those specifically stated values. A domain definition (constraint) can also be stated between permissible values of entity properties in different data types.

28. Entity Key Property: An entity property or collection of properties whose values uniquely identify objects belonging to the set of objects in the data type or entity set.

29. Relationship: A connection between two entities that mutually associates them.

30. Relationship Cardinality: A designation on the connection between modeled data types that indicates how many of one type of business object can be related to the other business object.

32. Entity Type Description: Specifies and describes the properties that comprise the business object about which the firm needs to retain data.

33. A PATIENT Entity Type Description: Patient__________________________ SOCIAL_SECURITY_NUMBER NAME STREET_ADDRESS CITY STATE TELEPHONE_NUMBER DOCTOR_ID_NUMBER NEXT_OF_KIN NEXT_OF_KIN_STREET NEXT_OF_KIN_CITY NEXT_OF_KIN_STATE NEXT_OF_KIN_TELEPHONE

34. Logical Data Stucture (LDS): A list of data elements for entity properties that meet a reasonable need for data about a data type inside and outside a company.

36. Subject Areas: A major topic of interest to the enterprise that helps it fulfill its mission, such as distribution, project financing, etc. Good data modeling divides the enterprise into manageable units called subject areas.

37. Subject Areas: Subject areas encompass a manageable set of objects that support the overall mission of the enterprise from the perspective of that subject area. Subject areas (sometimes called data classes) relate to organizational subjects rather than to computer applications.

38. Course Subject Areas: A systematic method of identifying subject areas is to start by producing a decomposition of functions. A sub-function of the functional decomposition is treated as a subject area with an accompanying E/RD created for it to identify and model the sub-function's data types as E/RD entities and relationships. The essence of what constitutes a business is its functions and the data types they comprise, use, serve, and that serve them.

40. Good Data Modeling Seeks To: 1. Identify the different data types for the business. 2. Produce entity type description for those data types. 3. Specify the relationship between those data types including cardinality.

41. Data Types in Multiple Subject Areas: A data type that is part of more than one subject area provides a view of it from the context of that subject area. The appearance of a data type in multiple subject areas helps identify all properties of the data type.

42. Sufficiency Requirement: 1. Determine all subject areas of which a data type is part. 2. Create a partial entity type description for each subject area. 3. Accumulate all data type properties in a single full entity type description.

45. Entity/Relationship (E/RD) Modeling Method: Provides a convenient and descriptive way of portraying the conceptual view of data types in their subject areas.

48. E/RD Relationship: A relationship can be just a line connecting to entities or an object that exists because two entities have some relationship themselves.

49. Identifying All Data Typesfor the Business: Consists of creating E/RDs for all subject areas that contain the data types that support the business in those subject areas.

53. Design Dictionary: A recording method used to define users, processes, data, and relationships between users and other users or users and data for how the graphical icons and connections represent real-world objects and activities.

54. Dictionary Entries for Graphs and Icons: The dictionary is often used to describe graphic models, what icons on the graphic model represent, and the purpose for connections between icons.

55. Design Dictionary Formatand Conventions Prescribe: A narrative be included where possible. Diagrams appear where necessary. Definitions be inserted for all diagram components. Individual entries be integrated. Adherence to conventions of punctuation. Summaries appear where appropriate.

56. CASE Systems: Tools used as instruments to make modeling methods, techniques, and procedures operational.

57. Advantages of Using CASETools during the Analysis Phase: More comprehensive and integrated recording of investigation results. Uniform recording of analysis for all systems. Diagram display of analysis results in a form easily explained to users. Easy navigation through analysis specifications. Easily modified entries during the iterative actions of analysis.

58. Using E/RDs During JADS: A JAD may be set up to include all of the users from a particular subject area to create and/or review E/RDs and entity type descriptions for all data types included in that subject area. JADS can contribute to the accuracy and completeness of the data model in a shorter period of time than traditional data modeling methods.