- 1 - © Houman Younessi 2010 MGMT 6170 - Advanced Systems Analysis and Design Convener: Houman...

- 1 -© Houman Younessi 2010MGMT 6170 - Advanced Systems Analysis and Design

Convener:

Houman Younessi

1-860-548-7880

[email protected]

Lecture 4

Advanced Systems

Analysis and DesignFall 2010


System ModelingIn SE, we have an array of notations and diagrams for modeling in each of these three views.

Structure ModelingEntity Relationship Diagrams, Formal Structural Models (e.g. Z, Object Z or VDM), Class Diagrams,…

Transformational ModelingTransformational Relations(Functional Specification), Activity Diagrams , Data Flow Diagrams (with specification), Flow Charts, …

Causal (Dynamic) ModelingSequence Diagrams, Collaboration Diagrams, State-charts (State Transition Diagrams), Petri-nets, Entity Life Histories,…


Structure modeling is modeling of things and their situational relationships. A photograph is a good structure model. It shows things that were there when the picture was taken and how they were situated with respect to one another.

We can similarly compose diagrams or other models of a problem situation in which we depict all the relevant things and relationships.

There are many ways to do this. We shall discuss the three most popular and prevalent of these. Namely:

Entity Relationship Modeling which is used mainly for database design

Formal Schemas and Formal Object Schemas (using Z and Object Z)

Class Diagrams (using UML) used mainly as part of object oriented modeling

Structure Modeling


Entity Relationship (ER) Modeling:

This is an informal (or semi-formal) approach to structure modeling in which a situation is studied so that static and persistent elements in it are identified, along with their static relationships. A collection of like elements is called an entity. A mapping of elements of one entity onto another entity (or itself) is called a relationship.

Entities are defined in terms of a name and a set of attributes. Relationships are defined in terms of a verb phrase (e.g. works-for) that establishes the nature of the mapping between the entities.

The results of ER modeling are almost always shown using diagrams. There are many different conventions. In the absence of an industry standard, we use a popular one here of my preference.

Structure Modeling


Example:Employee

Name:

SSN:

Salary:

DepartmentName:

Location:

Budget:

Works-Form 1

This means that there are many elements belonging to the set Employee (i.e. many persons employed) each is mapped into (has a relationship with) only one element belonging to the entity Department (a specific department). The relationship is that this particular employee works for one specific department. For each employee we keep his or her name, social security number and current salary. For each department we keep the name of the department, its location and its budget.

You will learn (or may have already learned) a lot more about this modeling approach in your database course.

Structure Modeling


Formal Object Schemas: Object Z

Stack[T]max:N

items: seq T

#items max

INITitems = ‹ ›

Push( items)

#items < maxitems’ = ‹item?›⁀items

item?:T

Structure Modeling


Pop( items)

item! ‹ ›items = ‹item!›⁀items’

item!:T

top( items)

item! ‹ ›items’ = items

item!:T

Structure Modeling


There are many different approaches to causal modeling. Whilst they all attempt to do the same thing, they are not all of the same level of capability, formality, ease of use or learnability. In this course we cover a number of popular approaches to causal modeling, including:

Entity Life Histories

The UML suite of dynamic modeling facilities, which include

Petri-nets

Sequence diagrams

Collaboration diagrams

State diagrams

Causal Modeling


Entity Life Histories

These are diagrams that depict the various states of a class or type of object from inception to demise. Usually used in relation to persistent database “entities”, they can become overwhelmed if the states are too numerous or the object can possess concurrent states. They also do not necessarily depict the events that lead to state transitions.

EMP

CREATE INIT UPDATE REPORT RETIRE ARCHIVE* *

Causal Modeling


Petri nets:

Petri nets are a formal graphical approach to causal modeling. They improve on the capabilities of state diagrams by allowing for proper description of some major issues in concurrency such as synchronization, deadlocks and conflicts.

Petri nets are composed of two types of nodes and one type of arc. The two types of node are called places and transitions. The arc is called an event. A fourth artifact called a token, when located inside a place, marks it as enabled.

Causal Modeling


A token ( ) inside a place indicates that the place has satisfied all pre-conditions for causing an event to occur. Such a place is called “enabled”

p1

p2 p3

p4

p5

t1

t2

t3

A Petri net composed of five places P={p1,p2,p3,p4,p5} and three transitions T={t1,t2,t3}

A transition takes place only when all places leading to it are enabled. Such a transition is called an enabled transition.

Causal Modeling


The system stops here.

A transition takes place to p2. But t2 is not enabled as p3 is not enabled.

p5

p1

p2 p3

p4

t1

t2

t3

P1 is enabled, thus enabling t1

Causal Modeling


p5

p1

p2 p3

p4

t1

t2

t3

Causal Modeling


p5

p1

p2 p3

p4

t1

t2

t3

p’1

p’2p’3

p’4

t’1

t’2

t’3

Conflict

?

Causal Modeling


p5

p1

p2 p3

p4

t1

t2

t3

Deadlock

?

Causal Modeling


Transformation modeling is the third modeling view. It answers the question “how”.

Depending on level of granularity there are many techniques. Including:

Abstraction Level:

Dataflow Diagrams

Activity Diagrams

Low Level:

Pseudo-code

Flowcharts

etc.

Not part of UML

Transformational Modeling


Flow charts

Flow charts depict the flow of control. They show how operations are performed and decisions made by depicting how the control in the program is exchanged from the beginning to the end of all paths of interest.

Flow charts show how the program works.

Flow charts are composed of a number of node types and one type of arc. The node types are:

Start/End node Transformation node Decision node Link node Special processing nodes Logic nodes



Flow charts can be high level or low level

High level flow charts depict the flow of control at a high level of granularity, such as the organization or the entire system. Low level ones usually depict the flow of control in a specific program unit.

The difference between a high level and low level flow chart is that in a low level flow chart all transformational nodes contain transformations that can not be usefully broken down to simpler flowcharts themselves. By this we mean doing so would produce transformation at a lower level of granularity than that of the target programming language.



Flow chart nodes:

Start/End nodes: These mark the beginning and end of a flow within a flowchart

Transformation nodes: These show a logical step taken

Terminator

Transformation Alternate transformation

Manual transformation



Decision nodes: These show alternate conditions or paths the flow may take

Link nodes: These connect various parts of the diagram (e.g. continue on next page)

Logic nodes: These are logical operators such as AND, OR and NOT

Condition

AND OR NOT

On page connector

Off page connector



Special processing nodes: These are nodes that depict specific large scale processing or machine interaction. Useful in the early days when flowcharting was amongst the only modeling methods available, they are now largely disused.

Manual input

Disk Other mag. storage

Stored data Punched tape Punched card

Seq. Access device

Console or display

Extract Merge Sort Collate Internal storage

Delay



Start

End

Read N

N>0

T

F

Read A,B

A=A+B N=N-1

N=0F

T

Write A



Data Flow DiagramsData flow diagrams depict the flow of data. They show how data received as input is changed to outputs by the various operations performed.

Data flow diagrams show how the data changes.

Basic data flow diagrams are composed of a number of node types and one type of arch. The node types are:

External Entities (Sources and Sinks) Processing node

Data-stores Link nodes



External entities (sources and Sinks): These are entities outside the scope of our focus that provide the inputs from the outside or receive the outputs generated. They are labeled by a noun or an object or class name.

Process nodes: These depict the processing that is done to the inputs into that process to form the output. Usually these nodes are labeled by a verb phrase representing the nature of the processing to be done and a number sequence depicting the process and its level

Customer

Book seatBook seat

1.4.71.4.7



Data-stores: These are buffers where interim outputs generated are stored for future usage. Data-stores are usually named.Link nodes: They connect the various parts of the diagrams to yield a less cluttered result. They are usually numbered or carry a symbol.

Primary Buffer

The only arc is called a dataflow and it depicts the flow of data (as input into or output from) an external entity or process. They are usually named.

client address

22



Example DFD

1.2.1

Validate Sell

1.2.2

Prepare SX Transaction

1.2.3

Register Transaction

Account

Invalid Req. Advice

Transaction A

dvice

Sell Validation

Trans. Confirmation

Sell Details

Account Update

Sell Advice

No. of Stock owned

Account Sell

Market Stock Price

Sell Stock; Level 3



Data Flow diagrams may depict a situation at multiple levels of granularity. By that we mean a process in a data flow diagram may be decomposed into an entire new dataflow diagram at a lower level, and so on. At each lower level, there will be more detail of the model visible. Conversely, one can say that a higher level process can be described in terms of a dataflow diagram composed of simpler, lower level processes, data flows and data-stores. However this decomposition process must stop at some stage. At that stage we shall still have a dataflow diagram that only depicts the transformation of inputs to outputs of various processes. It however does not say HOW each leaf level process should achieve this. This may be obvious but is not defined.



Dataflow diagrams are more so a mechanism for abstraction than a transformational modeling technique. They must be accompanied by a complementary mechanism that defines the leaf level transformations. Something like a flowchart of each leaf process, a pseudo-code, mathematical equation, truth table or formal definition is needed.

Important Note:



Pseudo-code:begin

Read r,a;

Declare x,y;

if { (a) L.T. 0

a=(-1)*a; };

Set x to r*sin(a);

Set y to r*cos(a);

Write x;

Write y;

end

Convert to Cartesian

1.5.6r

a

x

y



Mathematical expression:

)cos(

)sin(

ary

arx

Convert to Cartesian

1.5.6r

a

x

y

Desc. For 1.5.6



Activity diagrams depict the processing aspects of the system. They are similar to flowcharts except:

ACTIVITY DIAGRAMS

Activity charts allow synchronization

They are similar to dataflow diagrams except:

Transition between activities is via conditions not data. Activity charts allow synchronization



Order ProcessingFinance

Receive

Order

Receive

Supply

Select Outstanding order item

Assign Goods to

OrderAssign Item to Order

Reorder

Item Add Remainder

to Stock

Check Line Item

Cancel Order

Check order

Authorize payment

[failed][succeeded]

Dispatch Order [Stock assigned to all line items and payment

authorized]

*[for each line item on order]

* [for each chosen order item]

[in stock]

[all outstanding order items filled]

[notify supply]

[out of stock]

Stock Manager



Structure Transformation Causality

Objects

Classes

Relationships

Inputs

Outputs

Transformations

Events

States

Sequences

ENCAPSULATION

- 1 - © Houman Younessi 2010 MGMT 6170 - Advanced Systems Analysis and Design Convener: Houman...

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