Automatic Generation of Questionnaires for Managing Conﬁgurable BP

Models

A. Jim´enez-Ram´ırez

, B. Weber

, I. Barba

and C. Del Valle

University of Seville, Dpto. Lenguajes y Sistemas Inform´aticos, Seville, Spain

University of Innsbruck, Department of Computer Science, Innsbruck, Austria

Keywords:

Conﬁgurable Business Process Models, Classiﬁcation Trees, Questionnaires.

Abstract:

Managing large collections of business process (BP) models is increasingly being necessary for organizations.

For this, conﬁgurable BP models can be used for managing these BPs while allowing analysts to understand

what these BPs share and what their differences are. Before the execution of the conﬁgurable BP model, a BP

model has to be selected from it. This selection is typically performed by an analyst who manually individual-

izes the model in order to address the business requirements. Unlike existing approaches, we propose a totally

automated method to create a questionnaire-based application for guiding a business expert on individualizing

a model.

1 INTRODUCTION

A Business Process (BP) can be deﬁned as a set

of activities which are performed in coordination in

an organization to achieve a business goal (Weske,

2007). These activities can be manual activities, other

BPs, or even pieces of software. Nowadays, in or-

der to support BPs, BP Management (BPM) embraces

methods, techniques, and software to design, enact,

control, and analyze operational processes involv-

ing humans, organizations, applications, and other

sources of information (van der Aalst et al., 2003).

Such management generally follows a strict method-

ology to ensure the quality of the information systems

which are created. Typically, the traditional BPM life

cycle (Weske, 2007) includes four phases, i.e., pro-

cess design & analysis (i.e., a design of the BP is

created following the requirements), system conﬁg-

uration (i.e., the software deﬁned in the BP design is

implemented), process enactment (i.e., the software

is executed following the BP design) and evaluation

(i.e., monitoring information or logs are analyzed to

look for design improvements) (Weske, 2007).

It becomes increasingly common for organiza-

tions to deal with large collections of BP models

(e.g., due to company mergers (Rosa et al., 2012),

BP models extracted from declarative speciﬁcations

(Jimenez-Ramirez et al., 2013), etc.). Therefore, as

shown in (Dijkman et al., 2012), more and more re-

search is done on BP model collections. In literature,

different techniques are proposed to manage such col-

lections (Dijkman et al., 2012). Among others, vari-

ant management techniques (Rosemann and van der

Aalst, 2007; Gottschalk et al., 2008; Hallerbach et al.,

2010) keep track of BP variants of a similar BP that

co-exist within a collection. Such collections are typ-

ically represented as a conﬁgurable BP model (Rose-

mann and van der Aalst, 2007; Rosa et al., 2012), i.e.,

a modeling artifact that captures a family of BP mod-

els in an integrated manner and that allows analysts

to understand what these BP models share, what their

differences are, and why and how these differences

occur. Conﬁgurable BP models include speciﬁc nodes

called conﬁgurable nodes which represent the varia-

tion points of the model.

Before the organizations can execute a conﬁg-

urable BP model, it needs to be individualized, i.e.,

a single BP model has to be selected from it. There-

fore, a new phase, namely conﬁguration & individu-

alization, is deﬁned in the BPM life cycle between the

process design & analysis and the system conﬁgura-

tion phases (La Rosa et al., 2008).

1.1 Problem Statement

Generally, in order to individualize a conﬁgurable BP

model (cf. Fig. 1 (1)), the analyst considers the busi-

ness requirements (i.e., which are typically speciﬁed

by the business expert, cf. Fig. 1 (2)) and manu-

ally performs the individualization (i.e., she selects

709

Jiménez-Ramírez A., Weber B., Barba I. and Del Valle C..

Automatic Generation of Questionnaires for Managing Conﬁgurable BP Models.

DOI: 10.5220/0004921407090714

In Proceedings of the 6th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2014), pages 709-714

ISBN: 978-989-758-015-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Configurable BP Model

Manual

Individualization

Analyst

Specific Business

Requirements

Business

Expert

Potential Discrepancies

Restricted Flexibility

BP Model

to be executed

Time Consuming

Figure 1: Motivation.

one of the variants which exist in the conﬁgurable

BP model, cf. Fig. 1 (3)). Therefore, this manual

task can be time-consuming and, in addition, it can

lead to discrepancies between the business expert and

the selected BP model. Furthermore, since the dif-

ferent BP models of a conﬁgurable BP model share

many commonalities (Rosemann and van der Aalst,

2007) (even in the initial parts of them), selecting

a single BP model at conﬁguration-time unnecessar-

ily restricts the ﬂexibility especially when the context

might change (cf. Fig. 1 (4)).

1.2 Contribution

The current work proposes a method for support-

ing the business expert in individualizing the model

through a questionnaire-based approach, i.e., a se-

quence of questions each one created for individualiz-

ing a part of the model (La Rosa et al., 2008). Taking

a conﬁgurable BP model as starting point (cf. Fig. 2

(1)), the questionnaire which is automatically gener-

ated consists of different questions written in the busi-

ness language (i.e., using properties that can be mea-

sured in the BP models of the conﬁgurable BP model

and that have enough semantic to be understandable

by the business expert, cf. Fig. 2 (2)). Therefore, the

business expert can individualize the models herself

by answering questions (cf. Fig. 2 (3)) without the

intermediation of the analyst. Furthermore, the gen-

erated questionnaires are intended to individualize the

model in an incremental way, i.e., guiding the execu-

tion of the conﬁgurable BP model. Therefore, the pro-

posed method starts individualizing initial parts of the

model and iteratively individualizes further succeed-

ing parts during run-time, i.e., when more information

is available to take decisions (cf. Fig. 2 (4)).

Unlike existing approaches (La Rosa et al., 2008;

Rosa et al., 2009), the current work proposes a

method for automatically generating questionnaires

for managing conﬁgurable BP models.

In this work, a preliminary study of the aforemen-

tioned process (cf. Fig. 2) is conducted. The main

contributions of this paper are: (1) the automatic gen-

eration of questionnaires for a conﬁgurable BP model

and (2) the method for using such questionnaires at

run-time for guiding the execution of a conﬁgurable

BP model.

This paper is organized as follows: Sect. 2 intro-

duces backgrounds on related areas, Sect. 3 shows

how the questionnaires are created from a conﬁg-

urable BP model and how they can used at run-time,

and Sect. 4 includes some conclusions and future

work.

2 BACKGROUND

In this paper, BP models (cf. Sect. 2.1) are consid-

ered as parts of conﬁgurable BP models (cf. Sect.

2.2). Such conﬁgurable BP models are used as start-

ing point for automatically generating questionnaires

(cf. Sect. 2.3) which will individualize them.

2.1 Business Process Models

BPs are commonly used to coordinate activities be-

tween organizations. To deal with BPs, in this work

the BP graph deﬁnition (cf. Def. 1) introduced in

(Rosa et al., 2012) is used.

Deﬁnition 1. A BP Graph G = (gid, N, Pairs) is iden-

tiﬁed by gid and consists of a set of pairs of nodes

n ∈ N, i.e., Pairs. Each pair denotes a direct edge

between two nodes in the graph. A node n ∈ N is a

tuple < nid, l, t > where nid is an unique identiﬁer of

a node in the graph, l is its label, and t is its type.

Such deﬁnition of graph allows to represent a

BP model in many different BP languages (BPMN,

2011), e.g., BPMN or EPC (cf. Example 1). As an ex-

ample, the types of nodes (i.e., t) in BPMN language

(BPMN, 2011) are ’activity’, ’event’, or ’gateway’. A

node of type ’gateway’ allows labels (i.e., l) ’AND’,

’OR’, ’XOR’, etc., while ’event’ nodes allow ’start’

and ’end’ labels.

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Delayed Decissions

Business Language

Configurable BP Model

Automatic

Business

Expert

Answers

Guided Execution

Partial BP Model

Questionnaire

Figure 2: Overview of our contribution.

Book hotel

Select clothes

Pack luggage

0 1 2 3 4 5

select

(b) BP Graph (BPMN)

pack

book

(a) Gantt Chart

Figure 3: A schedule (a) as a BP Graph (b).

Example 1. Figure 3 (a) shows three activities which

are scheduled to prepare a holiday and they are de-

picted as a Gantt chart (Gantt, 1913). The activ-

ities ’book a hotel’, ’select the clothes’ and ’pre-

pare the luggage’ are considered. In addition, Fig.

3 (b) shows the related BP graph using BPMN.

This graph consists of the following 7 nodes (cf.

Def. 1): <1, start, event>, <2, AND, gateway>,

<3, book, activity>, <4, select, activity>, <5, AND,

gateway>, <6, pack, activity> and <7, end, event>;

which are paired (cf. Def. 1) as follows: (1, 2), (2, 3),

(2, 4), (3, 5), (4, 5), (5, 6), and (6, 7).

2.2 Conﬁgurable BP Model

Typically, different BPs (cf. Def. 1), also called

variants, can be performed in scenarios which en-

tail high variability. In most cases these plans share

many commonalities. Hence, these variations can be

combined in a conﬁgurable BP model (i.e., a mod-

elling artifact that capture a family of BP models in

an integrated manner) leading to a compact represen-

tation (Rosa et al., 2012; Rosemann and van der Aalst,

2007; La Rosa et al., 2008; van der Aalst et al., 2006).

Generally, conﬁgurable BP models allow analysts to

understand what these variations share, what their dif-

ferences are, and why and howthese differences occur

(Rosemann and van der Aalst, 2007).

Conﬁgurable BP models are typically created by

hand (1) from scratch, (2) from an existing BP model

by including possible adaptations (Gottschalk et al.,

2008), or (3) by merging some BP models related to

the same or similar goals which already exist (Rosa

et al., 2012; Jimenez-Ramirez et al., 2013). In the

last case, the source BP models need to be compared

and merged, which might result in a tedious, time-

consuming and error-prone process if it is performed

by hand (Rosa et al., 2012). To overcome these prob-

lems, there exist approaches focused on automatically

merging different BP models in a conﬁgurable BP

model (Rosa et al., 2010; Rosa et al., 2012).

Conﬁgurable BP models can be represented by

conﬁgurable BP graphs, which are deﬁned (cf. Def.

2) based on (Rosa et al., 2012).

Deﬁnition 2. A Conﬁgurable BP Graph CG =

(G, E2I, N2LI) consists of: (1) a graph, G =

(gid, N, Pairs) (cf. Def. 1), (2) a function E2I that

maps each edge e ∈ Pairs to a set of BP graph identi-

ﬁers (i.e., E2I identiﬁes which branches of CG belong

to each source BP graph which is merged in CG), (3)

a function, N2LI that maps each node n ∈ N to a set

of pairs < gpid, l > where gpid is a BP graph iden-

tiﬁer and l is the label of node n in graph gpid (i.e.,

N2LI identiﬁes which nodes, with the corresponding

label, belong to each graph which is merged in CG).

A conﬁgurable BP graph includes conﬁguration

nodes for those points where the BP graphs which

are included differ (cf. Example 2). Therefore, each

branch and node of the conﬁgurable BP graph can be

related either to one or more BP graphs. To store these

relations, each branch/node of the conﬁgurable BP

graph includes identiﬁers related to the correspond-

ing BP graph (i.e., E2I function). In addition, nodes

also store the associated label related to each identiﬁer

(i.e., N2LI function).

Example 2. Figure 4 shows 2 graphs which are

merged into a conﬁgurableBP model

. The ﬁrst gate-

way in Fig. 4(b) is a conﬁgurable node which corre-

sponds to an ’OR’ gateway in the process 1 (it does

not explicitly appear) and an ’AND’ gateway in the

process 2.

2.3 Questionnaires

Questionnaire models (Rosa et al., 2009) are gener-

ally created to support the user during the individual-

As there is not ambiguity, some labels are not shown

(i.e., they are the same as in the branch).

AutomaticGenerationofQuestionnairesforManagingConfigurableBPModels

711

(a) Graphs (BPMN)

pack

select

book

select

book

select

x x

(b) Configurable BP Graph

1,2

<1,or>

<2, and>

select

pack

book

pack

Figure 4: Two BP graphs (a) are merged into a single con-

ﬁgurable BP graph(b).

ization of the conﬁgurable BP models. The main ben-

eﬁts of using them are: (1) they guide the user in such

a way that choices are presented in a proper order and

(2) they avoid invalid conﬁgurations which may lead

to errors.

Typically, questions which are within a question-

naire are manually created. In addition, such ques-

tions are related to boolean facts which are associ-

ated to conﬁguration actions (the reader is referred to

(Rosa et al., 2009) for a review on interactive ques-

tionnaires). Therefore, each time a question is an-

swered, an action is ﬁred which individualizes a part

of the model. The sequence of answers to differ-

ent questions will individualize the conﬁgurable BP

model in such a way a single BP model is selected.

Unlike the work presented in this paper, question-

naires generally individualize conﬁgurable BP mod-

els before starting the execution and thus, unneces-

sarily restricting the ﬂexibility.

3 THE PROPOSED METHOD

In this section, the proposed method (cf. Fig. 5) is

described.

3.1 Conﬁgurable BP Model

As an initial step, to track the BP models during the

method, all of them are labeled (cf. Example 3).

Then, the conﬁgurable BP model (cf. Fig. 5 (2)) is

executed until a conﬁgurable node appears (cf. Fig. 5

(2)).

Example 3. For the sake of simplicity, the running

example of Fig. 6(a) comprises four BP models which

represent different ways of executing four activities

(i.e., A, B, C and D). Each BP model is label with

an integer. Furthermore, a group of properties of the

application domain for each BP model is provided

(cf. Fig. 6 (b) where time (T), beneﬁt (B) and risk

(R) properties are provided for each model).

Such

This properties are manually provided and must be

well-deﬁned to be understandable by the domain expert.

properties are related to the business language, e.g.,

T is related to how long the business is opened and

R refers to the maximum risk that the business can

afford.

The conﬁgurable BP model associated to the

aforementioned four BP models are depicted in Fig.

7(a). In this model, four different conﬁgurable nodes

are depicted with a bold diamond. In the ﬁrst conﬁg-

urable node, labeled as 1, two alternatives are possi-

ble. The left branch comprises variant 4 (i.e., where

activity A is not executed), and the right branch com-

prises variants 1 to 3 (where activity A is executed).

3.2 Generating Classiﬁcation Trees

When a conﬁgurable node is encountered we apply

a method for generating a set of questions related to

this node. Among other techniques as discrimination

or cluster analysis, the current method uses classiﬁca-

tion trees (i.e., models that predict the value of a target

variable based on several inputs variables) (Breiman,

1984) to predict which outgoing branch would cor-

respond to a given assignment of property value.

Speciﬁcally, for each conﬁgurable node which is en-

countered, a classiﬁcation tree is created (cf. Fig. 5

(3)) using the property values of the BP models as in-

put variables (cf. Example 4).

Example 4. Fig. 7(b) shows the classiﬁcation

tree which results from using the CART algorithm

(Breiman, 1984)

by providing the table of Fig. 1(b)

as input variables and the strings left and right as

target variables. As can be seen, in the resulting clas-

siﬁcation tree, the BP models for which T > 5 corre-

spond to the right branch. In contrast, the BP models

for which T ≤ 5 correspond to (1) the righ branch if

R ≤ 10, (2) to the left branch otherwise.

3.3 Generating Questions

A set of questions is then created for each decision

tree (cf. Fig. 5 (4)). For this, one question is auto-

matically generated for each intermediate node of the

decision tree, and the possible answers for the ques-

tion are the different labels which are written on the

outgoing branches of this node. These questionnaires

are in charge of narrowing down the variants of the

conﬁgurable BP model.

Though other techniques can be use for classifying, we

select the decision trees because of its hierarchical feature.

Different methods can be used to create a classiﬁca-

tion tree. The suitability and quality of each classiﬁcation

method depend on the characteristics of the data. This anal-

ysis is out of the scope of the current approach.

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Questions

Decission Tree

Next Configurable

Node

Execute until

Configurable

Node

Configurable BP Model

1-3,50

21,32

5,11,14

Generate

Decission

Tree

right

left

…

2. 3.

Create

Questions

Answer questions to individualize the model

…

Figure 5: The proposed method.

label

T B R

1 12 11 30

2 5 3 10

3 10 13 30

4 5 4 20

A B C D

A C

A B D

Figure 6: (a) Different BP models. (b) Table of BP model

properties.

≤5

right

>10

≤10

left

right

2 3 4

3,4

2,4

1,2,3

1,3,4

1,2

1,3

A B C D

a) b)

Figure 7: (a) Conﬁgurable BP model related to the BP mod-

els of Fig. 6(a). (b) Classiﬁcation tree for node 1.

The text of the questions are automatically gener-

ated from the information of the properties (cf. Ex-

ample 5).

Example 5. A simple questionnaire related to the de-

cision tree of Fig. 7(b) is shown in Fig. 8(a). Since

this decision tree has two intermediate nodes (i.e., T

and R), two questions are created. Moreover, since

each node has two branches, each question has two

options. Initially, only the question related to T is

enabled. Considering that the well-deﬁned business

properties stated that T is related to the closing time

of the ofﬁce, the generated question would look like

What time would you close the ofﬁce?. The second

question has to be answered only if the user selects

the second option of the ﬁrst question (i.e., In 5h. or

less) which is related to the branch T ≤ 5 of the deci-

sion tree.f

3.4 Incremental Conﬁguration

Once a questionnaire is resolved, the conﬁgurable BP

model is individualized by removing the BP models

that do not belong to the edge which result selected in

the questionnaire. Thereafter, the model is executed

until a new conﬁgurable node is reached (cf. Example

6).

Example 6. Supposing that the user selects the ﬁrst

answer of the ﬁrst question of the questionnaire of

Fig. 8(a) (i.e., In more than 5 h.), BP models 2 and

In more than 5 h. In 5 h. or less

What is the maximum ‘Risk’ affordable?

Can be over 10 Must be lower or equal to 10

A B

What time would you close the office?

Node1

Figure 8: (a) Questionnaire for node 1. (b) The resulting

conﬁgurable model after removing variants 2 and 4.

4 are removed from the conﬁgurable model since they

have a time property ”≤ 5”. This results in the con-

ﬁgurable BP model of Fig. 8(b). The second and

forth conﬁgurable nodes of Fig. 7(a) are not de-

picted in Fig. 8(b) since BP models 1 and 3 belong

to the same outgoing branches in these nodes, i.e., the

right branch. However, the third conﬁgurable node

requires to select one of the two branches, and then

a new questionnaire is generated. The conﬁguration

process continues until only one BP model remains

(i.e., representing one speciﬁc variant) in the conﬁg-

urable BP model.

4 CONCLUSIONS AND FUTURE

WORK

The manual individualization of conﬁgurable BP

models is time consuming and typically requires sup-

port by an analyst. Questionnaire-based approaches

are suitable methods to support the user while indi-

vidualizing these models. However, to the best of our

knowledge, there is not an automatic method for gen-

erating such questionnaires (Rosa et al., 2009).

In this paper an automatic method for generating

questionnaires is proposed based on the domain vari-

ables of the conﬁgurable BP model. The generated

questionnaires are proposed to be used to individu-

alize the model during its execution. The initial ex-

perimental results over a case of study are promising.

As future work we plan to (1) improve the semantics

of the questions which are created since they seem

too artiﬁcial and (2) conduct several case studies to il-

lustrate the feasibility of the proposed method at run-

time.

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REFERENCES

BPMN (2011). Business Process Model and Notation

(BPMN), Version 2.0. url http:/www.omg.org/spec/

BPMN/2.0/. [Online; accessed 1-June-2011].

Breiman, L. (1984). Classiﬁcation and regression

trees. The Wadsworth and Brooks-Cole statistics-

probability series. Chapman & Hall.

Dijkman, R., Rosa, M. L., and Reijers, H. A. (2012). Man-

aging large collections of business process models-

current techniques and challenges. Comput. Ind.,

63(2):91–97.

Gantt, H. (1913). Work, wages, and proﬁts. Engineering

Magazine Co.

Gottschalk, F., van der Aalst, W. M., Jansen-Vullers, M. H.,

and La Rosa, M. (2008). Conﬁgurable workﬂow mod-

els. International Journal of Cooperative Information

Systems (IJCIS), 17(2):177–221.

Hallerbach, A., Bauer, T., and Reichert, M. (2010). Captur-

ing variability in business process models: The provop

approach. Journal of Software Maintenance and Evo-

lution: Research and Practice, 22(6-7):519–546.

Jimenez-Ramirez, A., Barba, I., Del Valle, C., and Weber,

B. (2013). Generating multi-objective optimized busi-

ness process enactment plans. In Salinesi, C., Nor-

rie, M., and Pastor, s., editors, Advanced Information

Systems Engineering, volume 7908 of Lecture Notes

in Computer Science, pages 99–115. Springer Berlin

Heidelberg.

La Rosa, M., Dumas, M., and ter Hofstede, A. H. (2008).

Modelling business process variability for design-time

conﬁguration. Handbook of Research on Business

Process Modeling.

Rosa, M., Aalst, W., Dumas, M., and ter Hofstede, A.

(2009). Questionnaire-based variability modeling for

system conﬁguration. Software & Systems Modeling,

8(2):251–274.

Rosa, M. L., Dumas, M., Kaarik, R., and Dijkman, R. M.

(2010). Merging business process models. In 18th

International Conference on Cooperative Information

Systems (CoopIS ‘10) 2010, pages 96–113, Crete,

Greece. Springer-Verlag Heidelberg.

Rosa, M. L., Dumas, M., Uba, R., and Dijkman, R. M.

(2012). Business process model merging : An ap-

proach to business process consolidation. ACM Trans-

actions on Software Engineering and Methodology

(TOSEM).

Rosemann, M. and van der Aalst, W. (2007). A conﬁgurable

reference modelling language. Inform Syst, 32:1–23.

van der Aalst, W., Dreiling, A., Gottschalk, F., Rosemann,

M., and Jansen-Vullers, M. (2006). Conﬁgurable pro-

cess models as a basis for reference modeling. In

Bussler, C. and Haller, A., editors, Business Process

Management Workshops, Lecture Notes in Computer

Science, pages 512–518. Springer Berlin Heidelberg.

van der Aalst, W., ter Hofstede, A., and Weske, M. (2003).

Business Process Management: A Survey. In Proc.

BPM, pages 1–12.

Weske, M. (2007). Business Process Management: Con-

cepts, Languages, Architectures. Springer.

ICAART2014-InternationalConferenceonAgentsandArtificialIntelligence

714