1. Copyright 2001 Prentice-Hall, Inc. Object Oriented Aproach to System Requirements: Process Modeling 7.1 Chapter 7

2. Process Modeling  Graphically represent the processes that capture, manipulate, store and distribute data between a system and its environment and among system components  Data flow diagrams (DFD) –Graphically illustrate movement of data between external entities and the processes and data stores within a system 7.2

3. Process Modeling  Modeling a system’s process –Utilize information gathered during requirements determination –Structure of the data is also modeled in addition to the processes  Deliverables and Outcomes –Set of coherent, interrelated data flow diagrams 7.3

4. Process Modeling  Deliverables and outcomes (continued) –Context data flow diagram (DFD)  Scope of system –DFDs of current system  Enables analysts to understand current system –DFDs of new logical system  Technology independent  Show data flows, structure and functional requirements of new system 7.4

5. Process Modeling  Deliverables and outcomes (continued) –Project dictionary and CASE repository  Data flow diagramming mechanics –Four symbols are used  Square, Rounded Rectangle, Open-ended Rectangle, Flow arrow –Developed by DeMarco and Yourdan 7.5

6. Data Flow Diagramming Mechanics  Data Flow –Depicts data that are in motion and moving as a unit from one place to another in the system. –Drawn as an arrow –Select a meaningful name to represent the data 7.6

7. Data Flow Diagramming Mechanics  Data Store –Depicts data at rest –May represent data in  File folder  Computer-based file  Notebook –Drawn as two horizontal parallel lines –The name of the store as well as the number are recorded in between lines

8. Data Flow Diagramming Mechanics  Process –Depicts work or action performed on data so that they are transformed, stored or distributed –Drawn as a circle –Number of process as well as name are recorded 7.8

9. Data Flow Diagramming Mechanics  Source/Sink –Depicts the origin and/or destination of the data –Sometimes referred to as an external entity –Drawn as a square symbol –Name states what the external agent is –Because they are external, many characteristics are not of interest to us 7.9

10. Data Flow Diagramming Definitions  Context Diagram –A data flow diagram (DFD) of the scope of an organizational system that shows the system boundaries, external entities that interact with the system and the major information flows between the entities and the system  Level-O Diagram –A data flow diagrams (DFD) that represents a system’s major processes, data flows and data stores at a higher level 7.10

11. Developing DFDs: An Example  Context Diagram contains no data stores  Next step is to expand the context diagram to show the breakdown of processes 7.11

12. Data Flow Diagramming Rules  Basic rules that apply to all DFDs –Inputs to a process are always different than outputs –Objects always have a unique name  In order to keep the diagram uncluttered, you can repeat data stores and data flows on a diagram 7.12

13. Data Flow Diagramming Rules  Process A. No process can have only outputs (a miracle) B. No process can have only inputs (black hole) C. A process has a verb phrase label  Data Store D. Data cannot be moved from one store to another. E. Data cannot move from an outside source to a data store F. Data cannot move directly from a data store to a data sink G. Data store has a noun phrase label 7.13

14. Data Flow Diagramming Rules  Source/Sink H. Data cannot move directly from a source to a sink I. A source/sink has a noun phrase label  Data Flow J. A data flow has only one direction of flow between symbols. K. A fork means that exactly the same data go from a common location to two or more processes, data stores or sources/sinks 7.14

15. Data Flow Diagramming Rules  Data Flow (Continued) L. A join means that exactly the same data come from any two or more different processes, data stores or sources/sinks to a common location M. A data flow cannot go directly back to the same process it leaves N. A data flow to a data store means update O. A data flow from a data store means retrieve or use P. A data flow has a noun phrase label 7.15

16. Decomposition of DFDs  Functional decomposition –Act of going from one single system to many component processes –Repetitive procedure –Lowest level is called a primitive DFD  Level-N Diagrams –A DFD that is the result of n nested decompositions of a series of subprocesses from a process on a level-0 diagram 7.16

17. Balancing DFDs  When decomposing a DFD, you must conserve inputs to and outputs from a process at the next level of decomposition  This is called balancing 7.17

18. Guidelines for Drawing DFDs 1. Completeness –DFD must include all components necessary for system –Each component must be fully described in the project dictionary or CASE repository 2. Consistency –The extent to which information contained on one level of a set of nested DFDs is also included on other levels 7.18

19. Guidelines for Drawing DFDs 3. Timing –Time is not represented well on DFDs –Best to draw DFDs as if the system has never started and will never stop. 4. Iterative Development –Analyst should expect to redraw diagram several times before reaching the closest approximation to the system being modeled 7.19

20. Guidelines for Drawing DFDs 5. Primitive DFDs –Lowest logical level of decomposition –Decision has to be made when to stop decomposition 7.20

21. Guidelines for Drawing DFDs  Rules for stopping decomposition –When each process has been reduced to a single decision, calculation or database operation –When each data store represents data about a single entity –When the system user does not care to see any more detail 7.21

22. Guidelines for Drawing DFDs  Rules for stopping decomposition (continued) –When every data flow does not need to be split further to show that data are handled in various ways –When you believe that you have shown each business form or transaction, on-line display and report as a single data flow –When you believe that there is a separate process for each choice on all lowest-level menu options 7.22

23. Using DFDs as Analysis Tools  Gap Analysis –The process of discovering discrepancies between two or more sets of data flow diagrams or discrepancies within a single DFD  Inefficiencies in a system can often be identified through DFDs 7.23

24. Logic Modeling  Data flow diagrams do not show the logic inside the processes  Logic modeling involves representing internal structure and functionality of processes depicted on a DFD  Two methods –Structured English –Decision Tables 7.24

25. Modeling Logic with Structured English  Modified form of English used to specify the logic of information processes  Uses a subset of English –Action verbs –Noun phrases –No adjectives or adverbs  No specific standards 7.25

26. Modeling Logic with Structured English  Similar to programming language –If conditions –Case statements 7.26

27. Modeling Logic with Decision Tables  A matrix representation of the logic of a decision  Specifies the possible conditions and the resulting actions  Best used for complicated decision logic 7.27

28. Modeling Logic with Decision Tables  Consists of three parts –Condition stubs  Lists condition relevant to decision –Action stubs  Actions that result for a given set of conditions –Rules  Specify which actions are to be followed for a given set of conditions 7.28

29. Modeling Logic with Decision Tables  Indifferent Condition –Condition whose value does not affect which action is taken for two or more rules  Standard procedure for creating decision tables –Name the condition and values each condition can assume –Name all possible actions that can occur –List all rules –Define the actions for each rule –Simplify the table 7.29

30. Summary  Data flow diagrams (DFD) –Symbols –Rules for creating –Decomposition –Balancing  DFDs for Analysis  DFDs for Business Process Reengineering (BPR) 7.30

31. Summary  Logic Modeling –Structured English –Decision Tables 7.31

32. Copyright 2001 Prentice-Hall, Inc. Conceptual Data Modeling The ER Model 7.34

33. Learning Objectives Define key data-modeling terms Conceptual data model Conceptual data model Entity-Relationship (E-R) diagram Entity-Relationship (E-R) diagram Entity type Entity type Entity instance Entity instance Attribute Attribute Candidate key Candidate key Multivalued attributes Multivalued attributes Relationship Relationship Degree Degree Cardinality Cardinality Associative entity Associative entity 7.35

34. Learning Objectives Ask the right kinds of questions to determine data requirements for an IS Learn to draw Entity-Relationship Diagrams (ERD) Review the role of conceptual data modeling in overall design and analysis of an information system Discuss relationships and associative entities Discuss relationship between data modeling and process modeling 7.34

35. Conceptual Data Modeling  Representation of organizational data  Purpose is to show rules about the meaning and interrelationships among data  Entity-Relationship (E-R) diagrams are commonly used to show how data are organized  Main goal of conceptual data modeling is to create accurate E-R diagrams  Methods such as interviewing, questionnaires and JAD are used to collect information  Consistency must be maintained between process flow, decision logic and data modeling descriptions 7.35

36. Process of Conceptual Data Modeling  First step is to develop a data model for the system being replaced  Next, a new conceptual data model is built that includes all the requirements of the new system  In the design stage, the conceptual data model is translated into a physical design  Project repository links all design and data modeling steps performed during SDLC 7.36

37. Deliverables and Outcome  Primary deliverable is the entity- relationship diagram  There may be as many as 4 E-R diagrams produced and analyzed during conceptual data modeling –Covers just data needed in the project’s application –E-R diagram for system being replaced –An E-R diagram for the whole database from which the new application’s data are extracted –An E-R diagram for the whole database from which data for the application system being replaced are drawn 7.37

38. Deliverables and Outcome  Second deliverable is a set of entries about data objects to be stored in repository or project dictionary –Repository links data, process and logic models of an information system –Data elements that are included in the DFD must appear in the data model and conversely –Each data store in a process model must relate to business objects represented in the data model 7.38

39. Gathering Information for Conceptual Data Modeling  Two perspectives –Top-down  Data model is derived from an intimate understanding of the business –Bottom-up  Data model is derived by reviewing specifications and business documents 7.39

40. Introduction to Entity- Relationship (E-R) Modeling  Notation uses three main constructs –Data entities –Relationships –Attributes  Entity-Relationship (E-R) Diagram –A detailed, logical and graphical representation of the entities, associations and data elements for an organization or business 7.40

41. Entity-Relationship (E-R) Modeling Key Terms  Entity –A person, place, object, event or concept in the user environment about which the organization wishes to maintain data –Represented by a rectangle in E-R diagrams  Entity Type –A collection of entities that share common properties or characteristic  Attribute –A named property or characteristic of an entity that is of interest to an organization 7.41

42. Entity-Relationship (E-R) Modeling Key Terms  Candidate keys and identifiers –Each entity type must have an attribute or set of attributes that distinguishes one instance from other instances of the same type –Candidate key  Attribute (or combination of attributes) that uniquely identifies each instance of an entity type 7.42

43. Entity-Relationship (E-R) Modeling Key Terms  Identifier –A candidate key that has been selected as the unique identifying characteristic for an entity type –Selection rules for an identifier  Choose a candidate key that will not change its value  Choose a candidate key that will never be null  Avoid using intelligent keys  Consider substituting single value surrogate keys for large composite keys 7.43

44. Entity-Relationship (E-R) Modeling Key Terms  Multivalued Attribute –An attribute that may take on more than one value for each entity instance –Represented on E-R Diagram in two ways:  double-lined ellipse  weak entity 7.44

45. Entity-Relationship (E-R) Modeling Key Terms  Relationship –An association between the instances of one or more entity types that is of interest to the organization –Association indicates that an event has occurred or that there is a natural link between entity types –Relationships are always labeled with verb phrases 7.45

46. Conceptual Data Modeling and the E-R Diagram  Goal –Capture as much of the meaning of the data as possible  Result – A better design that is easier to maintain 7.46

47. Degree of Relationship  Degree –Number of entity types that participate in a relationship  Three cases –Unary  A relationship between the instances of one entity type –Binary  A relationship between the instances of two entity types –Ternary  A simultaneous relationship among the instances of three entity types  Not the same as three binary relationships 7.47

48. Cardinality  The number of instances of entity B that can be associated with each instance of entity A  Minimum Cardinality –The minimum number of instances of entity B that may be associated with each instance of entity A  Maximum Cardinality –The maximum number of instances of entity B that may be associated with each instance of entity A 7.48

49. Associative Entity  An entity type that associates the instances of one or more entity types and contains attributes that are peculiar to the relationship between those entity instances 7.49

50. Internet Development: Conceptual Data Model  Conceptual data modeling for Internet applications is no different than the processed followed for other types of applications  Pine Valley Furniture WebStore –Four entity types defined  Customer  Inventory  Order  Shopping cart 7.50

51. Summary  Process of conceptual data modeling –Deliverables –Gathering information  Entity-Relationship Modeling –Entities –Attributes –Candidate keys and identifiers –Multivalued attributes  Degree of relationship 7.51

52. Summary  Cardinality  Associative entities  Conceptual data modeling and Internet development 7.52