VARIOUS PROCESS WIZARD FOR INFORMATION SYSTEMS

Using Fuzzy Petri Nets for Process Definition

Jaroslav Prochazka, Jaroslav Knybel, Cyril Klimes

Department of informatics and computers, University of Ostrava, 30. dubna 22, Ostrava, Czech republic

Keywords: Process, workflow, wizard, process wizard, Petri net, fuzzy Petri net, IF-THEN rules.

Abstract: The new approach in information system automation is process or workflow management. For unskilled

user is important, when the business processes of company are described. Then, according to this

description are users led correctly in their work. The business (application) model can be caught in finite

state machines and its variations. Petri net can be used for process definition in process wizard. Sometimes

unclear state occurs, for its description can be fuzzy logic IF-THEN rules used. We explain what process

wizard is, what should contain and outline how it could be implement in IS QI. We also introduce Petri nets

with fuzzy approach for process description.

1 INTRODUCTION

Today’s open global market changes the nature of

business. Company that wants to be able to compete

must leave traditional organization structure and not

convenient leading methods. Company should be

focused on customer and should be managed by

market requirements. From an inner point of view,

company should focus on processes and team

cooperation. The base of all these changes is the use

of (new) information technologies. The processes

are the roots of every working organization. Modern

organization that wants to be able to compete on

market in global society should be based on

automation processes (Carda, Kunstová, 2001).

Workflow means whole or part automation of

enterprise process, during which are documents,

information or tasks handed over from one process

participant to another, according to a set of

procedural rules so, that it contributes the

performance of global enterprise objectives.

Workflow system defines, creates and manages the

process flow. This system is able to interpret process

definition, communicate with workflow participants

and if needed, run other applications (Hollingsworth,

1994). As a real workflow system, we consider the

one that provides:

– Graphical workflow design, which defines the

flow of tasks and activities, roles to activities.

– Rules defining the logic of process without

programming.

– Exception solving.

– Monitoring of process instances, measurement.

– Process simulation, testing, statistic reports.

– Database interface (from its IS).

– Document affiliation.

Enterprise infrastructure is set up by combination

of its processes. Unfortunately is this infrastructure

not completely described and documented, because

the major part are advances designed and held in

employees heads or are overspread through different

directives or are respected as an informal rules.

Some of them are between employees handed on

oral form. Any improvement or extension demands

infrastructure documentation first.

In recent state the process support in information

systems is not spread. There exist several solutions

on the market, where user can define company’s

processes and these definitions join with information

system’s functions. Such system can show, what was

previous process step or user can see possible

following steps according to current process state.

One of these tools is IS SAP and process tool ARIS,

other one is e.g. Baan IS. QI IS also contains support

for process management.

Unfortunately process or process participants can

include elements that are not clear. When we use

strict process definition, we are not able to capture

these elements. One approach how to include

unclear elements into process description is the use

of fuzzy approach.

235

Prochazka J., Knybel J. and Klimes C. (2006).

VARIOUS PROCESS WIZARD FOR INFORMATION SYSTEMS - Using Fuzzy Petri Nets for Process Deﬁnition.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - AIDSS, pages 235-242

DOI: 10.5220/0002441102350242

 SciTePress

Useful tool for process (workflow) management

is process wizard. This tool can lead user through

whole process according to his operations and

current system states.

Our research intention deals with process wizard

design for QI information system, so some practical

samples are shown in IS QI, but majority of

appointed ideas is usable generally. This paper is

promoted by research intention DAR of Czech

academy of science 1M6798555601.

1.1 QI and Processes

As said, QI information system contains also

implementation of workflow. Nowadays, in IS QI is

implemented process management without

possibility of automatic IS functions call. We briefly

introduce technological solution of QI system

function (form) implementation. Data is stored in

database. This database is only storage; it does not

implement any functions. Stored data does not make

sense at all, for the sake of security. The most

important part is application server (AS) – here is

data collected together. AS consists of data interface,

object server and stores application logics as well.

Application forms display user data. Important

component, which defines data in form, is called

data set (DS). It is user defined selection set and

represents part of application logics as well.

Win client comprises general programmatic

functions implementation (general form, general

report – so called functional objects). DB stores not

only user (application) data, but also concrete form

and report definitions – their size, position, included

components (buttons, fields, etc.), and connected

data sets. Before the called form is opened, the

general function of win client has to ask application

server for these definition data. This implicates, that

called form must exist, must be created first. QI

architecture depicts following figure 1.

Figure 1: QI application technology.

If we want to automate the process as deep, that

the system functions will be opened (generated)

automatically, there have to exist several variants of

given functions (forms) for each branch of process

or for different processes. Let us introduce an

example. One bill form with appropriate checks and

data fields is necessary for insertion to system. Other

one (with quite different checks and fields) we need

for supervisor’s authorization. It denotes, except

complete process description, to develop all possible

variants of programmatic functions (forms and

reports) that can be used in processes. Such

approach is neither effective nor practicable.

Besides, any change in process definition brings

revision of all programmatic functions supporting

this process. Therefore is our aim to design wizard

to generate called programmatic functions

automatically according to given templates or

patterns with possibility of user modification (what

data can user see and use).

2 PROCESS WIZARD

This chapter introduces what the wizard is, and what

are its features. The name could in reader invoke

mighty capability, but in IT, the wizard is usually

called a program or a component, which helps user

or developer to create or finalize some document,

application class, application component, form or

anything else. This is done step by step and it has its

beginning and its end.

We can define software wizard as component

with following qualities:

– All wizard data compose a single transaction.

– Steps are processed in sequence from given

beginning to given end.

– There exist one starting step, several

intermediate steps, and one ending step.

– The wizard validates its state before advancing

to the next step.

– There can be several different paths to reach the

ending step.

– It is possible to navigate back to review and

update values entered in previous steps.

– A wizard can be cancelled before completion.

QI process wizard should lead user through

whole process or through its important part. Every

form is based on given data set (DS), if there is more

than one DS included in form, there should be

hierarchy or synchronization of data sets. These

hierarchies and synchronizations have to be included

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

236

into wizard as well. From above facts result

fundamental assumptions for process wizard. For

dynamical form generation according to valid

process, we need:

1. Process description – description of application

domain, for this purpose can be used Petri net or

finite state machine (FSM).

2. User defined data – this is, what user want to

see, update or validate.

3. List of relevant DS (which can be used for given

form) including their hierarchy and possible

synchronizations.

4. Storage for application data and for model data

(process description) as well.

We come up from stored process description, user

defines application data, which he/she wants to work

with, then is generated (dynamically created) form

that matches all given requirements. Storing

mechanisms are already included in IS QI and also

are in all other IS.

Let us shortly discuss item 1. Formalisms based

on FSM are often used for process description. But

there exist also other tools for process modelling in

IT, namely UML activity diagrams, BPMN

(Business Process Management Notation), ARIS

notation and many others. One of our arguments,

why we have chosen Petri net for process modelling

is that these tools mentioned above do not have

mathematical apparatus as Petri nets do. The second

one is possibility of use fuzzy Petri net for unclear

process definitions. We used paper (Boerger, 2002)

for our decision-making as well. Chapter 3 deals

with process description and Petri nets.

We use in process wizard known and usual

approaches and technologies, these are Petri nets and

automatic code generation. Asset of our solution is

using them all together and also inclusion of fuzzy

approach into enterprise process modelling. Using

this solution, no UI screen forms need to be pre-

defined (implemented), they are automatically

generated whenever needed. Only templates for

generation have to be defined (see chapter 4.1). This

brings advantage for application customisation and

for change implementation. We believe that variable

visual process description and automatic generation

of UI screens and functions is better than to lock

processes into application code.

For user steps automation is process model

(description) used. User is led throughout his/her

working procedure. Thanks to fuzzy approach it is

possible to automate also some system decisions

where user otherwise should make decision. So it

brings more automation into information system.

But user can be still the one, who manages

application.

2.1 MVC Model Architecture

To insert concepts said in this paper to IT

architecture, we use really known MVC model. This

technology was first used in Smalltalk programming

language class library. It is typically used in user

interface (UI) programming, and it is also known

from Java Swing class library. Though this

technology is older than 20 years, it is still often

used in IS and web projects. Basic technology

feature is implementing program component using

three following classes:

– View: this object represents visual graphical

component.

– Controller: catches inputs via graphical

component and reacts on it (perform action on

model and view). Controller is binding between

model data and their graphical representation.

– Model: object represents graphical component

data (domain model).

Using MVC technology for implementing user

interface, it is common, that model is shared

simultaneously by more than one graphical

component (e.g. same data can be in textual grid

form but also in a graphical tree form).

In our case, the wizard, user works with, is a

view (the graphical component). Wizard can contain

also controller, which handles all user inputs and

perform these changes on model.

When we come back to the beginning of this

chapter and read once more the 7 points, which

define wizard, we realize, that model of wizard

should be a directed acyclic graph with weighted

edges. Model represents this graph with a modified

adjacency list. Each node of the graph corresponds

to one wizard step. A node either refers to or directly

stores domain data relevant to the step. Besides that,

a node holds references to the adjacent nodes.

Directed graph G is a pair G = (V, E), where

V = {v

, v

, …, v

} is a set of points called vertices

or nodes

E = {e

, e

, …, e

} is a set of lines or curves called

edges.

A directed acyclic graph is a graph with no

directed cycles. That is, for any vertex v, there is no

directed path starting and ending on v.

Using marked edges, we can describe every path

through graph. There exist several ways, how to

VARIOUS PROCESS WIZARD FOR INFORMATION SYSTEMS - Using Fuzzy Petri Nets for Process Definition

237

describe it. One is a list of names of edges; the

second one is a matrix or a table (see following

example, figure 2 depicts graph described by table).

Figure 2: Graph for login path.

Start (S) Login (L) End (E)

Start (S) rightLogin wrongLogin

End (E)

It is important to stress, that terminology of

graph theory is not unified, so above written

definitions can be little bit different from these

reader knows (Demel, 2002).

3 PROCESS DESCRIPTION

It is possible to describe application domain or

process transitions using finite state machines

(FSM). They are strictly formal and have

mathematical apparatus, thus they are suitable for

process modelling, simulation and automatic

generation. Although FSM is really spread, it has

several limitations, namely parallelism modelling,

distribute system modelling and huge state amount

for complex process description. Consequently

another formalism based on FSM is used: Petri nets.

Petri net concept is known from 1966, for

detailed description see (Peterson, 1981). This

concept comes out from decomposition of system to

subsystems described by finite state machines. These

machines work independently, but can be

coordinated. From its beginning, Petri nets has come

a long way, and nowadays are intensively studied.

Petri nets are used mainly for analysing, designing

and modelling parallel and distributed systems as

well as for parallel architecture description, compiler

or computer net description or programming

languages semantics description. Petri net is a

special biparital graph. It is composed from several

types of elements:

– Places: are used for expressing modelled system

states; circle usually represents places.

– Transitions: describe system changes; places are

usually represented by rectangles.

– Arcs: are unconditionally oriented and connect

place with transition or transition with place. Arc

cannot connect two places or two transitions.

Every ordered pair (place, transition) or

(transition, place) can be at most connected by

one arc. Arcs are usually represented by arrow.

– Inhibitory arcs: are special sort of arcs, this arc

can connects given place with chosen transition.

Inhibitory arcs are usually represented by line

with circle on transition’s side.

– Marking: represents actual system state using

tokens. Token is depicted by small circle and can

be used only in places. Every place can have

given number of modelled system tokens.

System states are modelled by mark flow. Initial

system state is usually called M

Formal definition for Petri net (sometimes called

P/T Petri net as Place/Transition) is following. Not

marked Petri net is a senary:

PTN = (P, T, A, IA, AF, IF)

Where:

P is a finite non-empty set of places

T is a finite non-empty set of transitions

P ∩ T = ∅ (their intersection is an empty set)

A ⊆ (P x T) ∪ (T x P) is a finite set of arcs

IA ⊆ (P x T) is finite set of inhibitory arcs

AF: (A ∪ IA) → N is an arc function

IF: P → N

∪ {ω} is initialisation function.

Figure 3 depicts example of Petri net with places P1,

P2, …, P7 and transitions T1, T2, …, T6, there

exists also inhibition arc (P3, T6). We can describe

initial net marking using given ordering by seven (1,

0, 1, 0, 0, 2, 1).

Figure 3: Petri net example.

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238

As we said above, we can extend Petri net

concept with unclear (fuzzy) elements for the

purpose of process modelling. When modelling

process sometimes we need depict state, which we

do not know if occurs or not (so called vagueness

phenomenon). Standard Petri net expresses states

using tokens. Token is included into place, if given

expression is true (1), if false (0), token is not

included. How to model unclear state? We can

employ fuzzy values (“small”, “much”, “middle”,

etc.) into Petri net. To work with these values we use

fuzzy logics, specifically IF-THEN rules. There

exist several ways, how to combine Petri nets with

fuzzy sets (Cardoso, Camargo, 1999). In one of

them, the net models reasoning systems. The

components comprise knowledge representation,

most of the time the transitions are associated with

fuzzy rules and the whole net depicts an expert or

control system (Chen, 1999, Gomes, 1999). The

second general approach devotes to the modelling of

dynamic physical systems and the transitions denote

possible state changes (Cardoso, 1999, Kunzle et al,

1999).

Our approach includes fuzzy logic by following

way. Token holds fuzzy set definition, arcs are

evaluated by natural language expressions and

finally transitions represent fuzzy relation

corresponding to given IF-THEN rule. Relation

creation depends on chosen inference method.

Closer explanation of inference methods is out of

range of this paper, for more information see

(Dvořák, Habiballa, Novák, Pavliska, 2003).

Fuzzy Petri net extends Petri net formal

definition with:

– D: set of expressions

– h: P → D function represents relation place –

expression

– a: P → [0, 1] function represents place value

–

, l: T → [0, 1] functions represent value

transition.

Let us introduce an example to clarify binding

between fuzzy IF-THEN rule and structure of fuzzy

Petri net. Our example records situation, when

subscriber wants to buy a lot of goods (costs a lot)

and has huge debit (not more than we tolerate),

system should make decision if sell everything or

not. IF-THEN rule will look like:

IF D is huge AND R is huge THEN P is low

Where: D is subscriber‘s debit

R is subscriber‘s purchase request

P is our permitted purchase amount

Following figure 4 depicts given fuzzy Petri net.

Figure 4: FPN implementing IF-THEN rule.

In outlined example system allows only low

amount purchase according to defined IF-THEN

rule. From this example is obvious that the system

can decide what to do without a human intervention.

System’s decision is based only on IF-THEN rules

and current values (D, R).

Petri nets are applied in many IT and automation

fields. We use Petri nets as a formal visual tool for

process modelling. Using fuzzy Petri net is possible

to shift decision making on system in some cases.

That is the reason, why we include fuzzy Petri net

into process modelling tool.

How to implement (source codes or approaches)

Petri nets even fuzzy Petri nets is out of the scope of

this paper, see e.g. (Dvořák, Habiballa, Novák,

Pavliska, 2003).

4 WIZARD DESIGN

As said above, Controller has to change model

according to user changes and user interface and

application data according to changes in a model.

Model (process) is represented by directed acyclic

graph. Each node of the graph corresponds to one

wizard step. A node either refers to or directly stores

domain data relevant to the step. Besides that, a node

holds references to the adjacent nodes (previous

nodes and possible following nodes). Wizard node

class should contain functions for:

– Adding outgoing edge of node (node can have

more than one outgoing edges),

– Returning the outgoing edge chosen for forward

transition,

– Storing and returning incoming edge used for

reaching current node, it is used for backward

transition,

– Validation (if node holds) – returns valid edge.

All nodes refer to application domain data. This

data is stored in a database, so Node class should

VARIOUS PROCESS WIZARD FOR INFORMATION SYSTEMS - Using Fuzzy Petri Nets for Process Definition

239

contain functions also for storing, loading and

validating data (e.g. right format, not empty values).

Besides this, each wizard step is UI screen form to

be generated and should depict application data.

Which screen form should be generated is defined

also in process model. Templates describe form

structure, controls and synchronizations. Each

wizard step need input data validation, if there is

anything wrong taking into account domain model

standing, the wizard does not traverse to another

step. Everything is again defined in wizard template

(see chapter 4.1). The last wizard step is

confirmation. Confirmation node does not define

any properties; it only applies wizard data to the

application domain model, when the wizard finishes.

The whole wizard transaction is then performed on a

domain model.

An edge defines the path from one wizard step to

another and holds the references to its source and

target nodes. An edge class should implement the

following important functions:

– Get source node for an edge,

– Get target node for an edge,

– Validation (if edge holds) – returns true if

forward move is allowed.

The wizard controller class should implement

following important functions:

– Get source node and current node,

– Get possible forward and backward traverses

(for moving to next or previous node).

We outlined wizard main functions. These are

edge validation rules, traversal commands, node’s

edge list and node and model synchronization. But

for our purpose and for user-friendly usage, we need

corresponding user interface (UI). Situation of

solution architecture depicts figure5

Figure 5: wizard architecture.

4.1 Implementing Wizard to QI

There exist two approaches how to implement

process wizard to QI information system. The first

approach is a complete generation of a UI screen

form (we need to work with) using templates. Screen

form is automatically generated always, whenever

called. The second one uses complete existing form

with marks. When user calls form, only specified

parts are shown, using marks. First we need to

examine the object model of QI, if there exist

appropriate objects for process wizard. Basic data

set dealing with processes and workflow in QI

information system is Workflow DS. The core of

this DS is object Process, this has relation to

Programming function object. Process wizard is a

software component, which reads the Petri net state

and calls/generates programming functions or its

subclasses according to the state (understand,

according to process description), see figure 5.

Process wizard is a component without its own

separate data objects; it reads existing ones. Because

of this fact, we do not need alter data model or data

sets. Now we can discuss 2 mentioned approaches.

The first approach – complete generation using

templates – has some prerequisites. We need to

group all possible data sets with abstract forms or

with processes. This step is important to reduce the

set of valid data sets. Why use all data sets, when the

bill form uses only customer data set, bill data set

and bill items’ data set. If we don’t do this, we can’t

prepare DS synchronizations. We use process

approach, it means only necessary data is shown and

used in every moment user performs the process.

Other data is neither requisite (shown) nor

accessible. That is one of process approach

advantage. It results the necessity of only reduced

count of data sets (only valid ones) at given process

step. For form design and element positioning are

used templates. Following example shows possible

structure of a template.

</container>

……

</container>

……

</synchronization>

ICEIS 2006 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

240

<column=”Amount” cType=”NOT-NEGATIVE”>

</control>

</form>

Container is a logical part of a form that can be

visually divided by line, empty space or by

bookmark. Generator (wizard) reads these templates

and generates required form using data from valid

data set. These templates can also contain sections,

where the data set synchronizations can be defined

as well as data controls (control section, the type of

control defines attribute cType, here the amount is

not allowed to be negative). This approach needs to

design all templates and implement into wizard

generative mechanism.

Second approach deals with existing forms, so

this doesn’t solve our objectives, but it is also one

possible approach. We need to design and

implement complete maximal forms with marks.

These marks are applied also on controls and data

sets together with synchronization. If no field from

DS is used, complete DS is used neither. Controls

are joined with particular fields. When we don’t

include field, control is included neither. Using this

approach we can simply solve data set

synchronizations and data controls. Controls and

synchronizations are in current application designed

by experienced engineers; it is not easy to automate

this step. Finally, calling the form is a sort of

customisation. Before calling form, we only need to

specify which marks use. This can we specified

directly in process description or by user while

operating. Final “generated” form has only data user

needs. The problem is that we need to design all

forms supporting company’s processes before using

application. These states of application are presently

almost the same only difference is the use of marks.

Wizard based on both approaches reads current

state from Petri net; it means the process description.

According to this description wizard calls

programming functions, this means calling marked

form or generating required form using template.

Wizard methods mentioned above should be

provided by formal mechanism (Petri net) and

should be provided via interface.

4.2 Process Modelling Tool

Process wizard and process modelling tool are two

different components. They can be implemented

together, but conceptually are two different modules

(subsystems), see figure 5. The first one – wizard is

designed for screen form calling. It can call existing

ones or generate them completely. We have

designed process wizard working prototype and now

we are finding the ways, how to implement

modelling tool. Process modelling tool supplements

wizard providing formal mechanism for process

modelling or description. Such mechanism catches

the process model and provides current state to

wizard via some interface. This interface can be

functional (functions or methods are invoked to

detect state) or data (states are handed over data).

As said process description encoded into formal

tool is read by process wizard and according to its

state is given form called or generated. Formal

mechanisms that are used for this purpose are FSM

or Petri nets. We have chosen Petri net for process

modelling because it has strict mathematical

apparatus that can be used for simulation and

algorithm modelling. The second reason was

possibility of use fuzzy Petri net for unclear process

definitions.

The first design issue is to map all process

elements to Petri net. It means that activities,

participants, sources (PC, people, money, machine),

documents (bill, contract, directive, guideline) and

all its relations have appropriate position in Petri net.

For example activity can be represented by place,

and when place includes the token, activity is

performed. This task we need to do before we

include fuzzy elements to Petri net (before we use

fuzzy Petri net). As we said above that the main data

set is the one called Process. We need to alter it to

include artefacts of Petri net. System should know

when can activity starts, when it ends, activity can

be also aborted without finishing etc. We introduce

new objects for these situations; see figure 6.

Figure 6: Fragment of Data Set Process.

Other design issues deals with inclusion fuzzy

elements to information system, mainly how to

represent fuzzy sets and rules, how to store them and

how to put all these elements together. We are

dealing with the use of existing LFLC system

developing at our university by team of professor

Novak. It can communicate with IS via XML. Other

solution is to implement it directly into information

system. Here are listed some fuzzy design issues:

VARIOUS PROCESS WIZARD FOR INFORMATION SYSTEMS - Using Fuzzy Petri Nets for Process Definition

241

1. Representation and data structure for fuzzy set.

2. Definition of IF-THEN rules.

3. Storing mechanism for fuzzy sets and rules.

4. Defuzzification (methods for defuzzificaton).

How to define data structure for fuzzy set is

fundamental part of solution. Using Process DS

there is possibility to represent fuzzy set as a normal

set with list of natural language expressions.

IF-THEN rules can be easily represented as

programmatic function – macro. Macro is a code in

IS QI macro-language, which is used for

programming controls and complex functions that

cannot be easy implement by using analytics

modelling. There exist several types of macros in QI

(client or AS macro, field or form macro, etc.) We

can write IF-THEN rule as a simple macro with a

new type RULE. Thanks to Process object and

Programmatic function object relation is ensured

connection between rules and Petri net. This can be

easy solution for first two issues.

The third issue deals with storing fuzzy sets and

rules. Both mentioned solutions use only system

parts and system tools (programmatic function, data

set, macro), thus are storing mechanisms already

implemented.

The last issue is defuzzification. For its

implementation advance, we can again use macro

that defines mathematical formula for getting

number from fuzzy set (centre of gravity, least of

maxima or mean of maxima).

5 CONCLUSION

This paper deals with workflow and its automation

in information systems. We briefly described QI

information system and a vision of a process wizard.

We outlined our objectives, process description

using formal tool (Petri net), wizard functions and its

design issues using MVC architecture.

We believe that the process automation and

automatic generation is the way, how to easy adapt

system for different users and implement changes.

While modelling process, unclear states can occur.

This is not possible to model by strict Petri net, thus

we introduced fuzzy IF-THEN rules and fuzzy Petri

nets. Fuzzy Petri nets can also automate some

decision making without a human intervention.

The last part deals with possible solutions for

process wizard and process modelling tool. The first

wizard solution is based on generation using

templates; the second one is based on existing

marked forms customisation. Process modelling tool

(Petri net) design issues are listed also, together with

fuzzy elements inclusion issues. Possible solutions

for QI information system were outlined.

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