Towards Process-driven Mobile Data Collection Applications

Requirements, Challenges, Lessons Learned

Johannes Schobel, Marc Schickler, R

udiger Pryss, Fabian Maier and Manfred Reichert

Institute of Databases and Information Systems,University of Ulm, James-Franck-Ring, Ulm, Germany

Keywords:

Process-aware Information System, Electronic Questionnaire, Mobile Business Application.

Abstract:

In application domains like healthcare, psychology and e-learning, data collection is based on speciﬁcally

tailored paper & pencil questionnaires. Usually, such a paper-based data collection is accomplished by a

massive workload regarding the processing, analysis, and evaluation of the data collected. To relieve domain

experts from these manual tasks and to increase the efﬁciency of the data collection process, we developed a

generic approach for realizing process-driven smart mobile device applications based on process management

technology. According to this approach, the logic of a questionnaire is described in terms of an explicit

process model whose enactment is driven by a generic process engine. Our goal is to demonstrate that such a

process-aware design of mobile business applications is useful with respect to mobile data collection. Hence,

we developed a generic architecture comprising the main components of mobile data collection applications.

Furthermore, we used these components for developing mobile electronic questionnaires for psychological

studies. The paper presents the challenges identiﬁed in this context and discusses the lessons learned. Overall,

process management technology offers promising perspectives for developing mobile business applications at

a high level of abstraction.

1 INTRODUCTION

Recently, smart mobile applications have been in-

creasingly used in a business context. Examples in-

clude simple applications (e.g., task management),

but also sophisticated analytic business applications.

In particular, smart mobile devices can be used for en-

abling ﬂexible mobile data collection as well (Pryss

et al., 2013). Thereby, data can be collected with

sensors (e.g., pulse sensor), communicating with the

smart mobile device (Schobel et al., 2013), or with

smart form-based applications (Pryss et al., 2012).

Examples of applications requiring such a mobile data

collection include clinical trials, psychological stud-

ies, and quality management surveys.

Developing a mobile data collection application

requires speciﬁc knowledge on how to implement

smart mobile applications. Furthermore, it requires

domain-speciﬁc knowledge, usually not available to

the programmers of these applications. Hence, to

avoid a gap between business needs and IT solutions,

continuous and costly communication between do-

main and IT experts becomes necessary. To improve

this situation, a framework for rapidly developing and

evolving mobile data collection applications is indis-

pensable. In particular, respective business applica-

tions should be easy to maintain for non-computer

(i.e., domain) experts as well. Our overall vision is to

enable domain experts to develop mobile data collec-

tion applications at a high level of abstraction. Specif-

ically, this paper focuses on the process-driven de-

sign, implementation, and enactment of mobile ques-

tionnaire applications, which support domain experts

with their daily data collection tasks.

As application domain for demonstrating the ben-

eﬁts of our approach we choose psychological stud-

ies. Here, domain experts mostly use paper-based

questionnaires for collecting required data from sub-

jects. However, such a paper-based data collection

shows several drawbacks, e.g., regarding the struc-

ture and layout of a questionnaire (e.g., questions may

still be answered, even if they are no longer rele-

vant or needed), or the later analysis of the answers

(e.g., errors might occur when transferring the col-

lected paper-based data to electronic worksheets).

To cope with these issues and to understand the

subtle differences between paper-based and electronic

questionnaires in a mobile context, ﬁrst of all, we

implemented several questionnaire applications for

smart mobile devices and applied them in real and

371

Schobel J., Schickler M., Pryss R., Maier F. and Reichert M. (2014).

Towards Process-driven Mobile Data Collection Applications - Requirements, Challenges, Lessons Learned.

In Proceedings of the 10th International Conference on Web Information Systems and Technologies, pages 371-382

DOI: 10.5220/0004970203710382

 SCITEPRESS

sophisticated application settings (Liebrecht, 2012;

Schindler, 2013). In particular, we were able to

demonstrate that electronic questionnaires relieve do-

main experts from costly manual tasks, like the trans-

fer, transformation and analysis of the collected data.

As a major drawback, the ﬁrst applications we had

implemented were hard-coded and required consid-

erable communication with domain experts. As a

consequence, these applications were neither easy to

maintain nor extensible. However, in order to avoid

a gap between the domain-speciﬁc design of a ques-

tionnaire and its technical implementation enacted on

smart mobile devices, an easy to handle, ﬂexible and

generic questionnaire system is indispensable.

From the insights we gained during the practi-

cal use of the above mentioned mobile applications

as well as from lessons learned when implementing

other kinds of mobile applications (Robecke et al.,

2011), we elicited the requirements for electronic

questionnaire applications that enable a ﬂexible mo-

bile data collection. In order to evaluate whether the

use of process management technology contributes

to the satisfaction of these requirements, we mapped

the logic of a complex questionnaire from psychol-

ogy to a process model, which was deployed to a pro-

cess engine. It then served as basis for driving the

execution of questionnaire instances at realtime. In

particular, this mapping allows us to overcome many

of the problems known from paper-based question-

naires. In turn, the use of a process modeling compo-

nent as well as a process execution engine in the given

context, raised additional challenges, e.g., related to

the process-driven execution of electronic question-

naires on and the mobile data collection with smart

mobile devices. The implemented questionnaire runs

on a mobile device and communicates with a remote

process engine to enact psychological questionnaires.

As a major lesson, we learned that process manage-

ment technology may not only be applied in the con-

text of business process automation, but also provides

a promising approach for generating mobile data col-

lection applications. In particular, a process-driven

approach enables non-computer experts to develop

electronic questionnaires for smart mobile devices as

well as to deploy them on respective devices in order

to collect data with them.

In detail, the contributions of this paper are as fol-

lows:

• We discuss fundamental problems of paper-based

questionnaires and present requirements regard-

ing their transfer to smart mobile devices.

• We provide a mental model for mapping question-

naires to process models. Further, we illustrate

this mental model through a real-world applica-

tion scenario from the psychological domain.

• We present a generic architecture for applications

running on smart mobile devices that can be used

to model, visualize and enact electronic question-

naires. This approach relies on the provided men-

tal model and uses process models to deﬁne and

control the ﬂow logic of a questionnaire.

• We share fundamental insights we gathered dur-

ing the process of implementing and evaluating

the mobile data collection application.

The remainder of this paper is structured as fol-

lows: Section 2 discusses issues related to paper-

based questionnaires. Further, it elicitates the re-

quirements that emerge when transferring a paper-

based questionnaire to an electronic version running

on smart mobile devices. Section 3 describes the

mental model we suggest for meeting these require-

ments. In Section 4, we present the basic architecture

of our approach for developing mobile data collection

applications. Section 5 provides a detailed discussion,

while Section 6 presents related work. Finally, Sec-

tion 7 concludes the paper with a summary and out-

look.

2 CASE STUDY

In a case study with 10 domain experts, we analyzed

more than 15 paper-based questionnaires from dif-

ferent domains, particularly questionnaires used in

the context of psychological studies. Our goal was

to understand the issues that emerge when transfer-

ring paper-based questionnaires to smart mobile de-

vices. Section 2.1 discusses fundamental issues re-

lated to paper & pencil questionnaires. Then, Section

2.2 elicitates fundamental requirements for their elec-

tronic mobile support.

2.1 Paper-based Questionnaires

We analyzed 15 paper-based questionnaires from psy-

chology and medicine. In this context, a variety of

issues emerged. First, in the considered domains, a

questionnaire must be valid. This means that it should

have already been applied in several studies, and sta-

tistical evaluations have proven that the results ob-

tained from the collected data are representive. In

addition, the questions are usually presented in a neu-

tral way in order to not affect or inﬂuence the subject

(e.g., patient). Creating a valid instrument is one of

the main goals when setting up a psychological ques-

tionnaire. In particular, reproducible and conclusive

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results must be guaranteed. Furthermore, a question-

naire may be used in two different modes. In the in-

terview mode, the subject is interviewed by a supervi-

sor who also ﬁlls in the questionnaire; i.e., the super-

visor controls which questions he is going to ask or

skip. This mode usually requires a lot of experience

since the interviewer must also deal with questions

that might be critical for the subject. The other mode

we consider is self-rating. In this mode, the question-

naire is handed out to the subject who then answers

the respective questions herself; i.e., no supervision is

provided in this mode

Another challenging issue of paper-based ques-

tionnaires concerns the analysis of the data collected.

Gathered answers need to be transfered to electronic

worksheets, which constitutes a time-consuming and

error-prone task. In particular, note that during the

interviews or the self-ﬁlling of a questionnaire, typo-

graphical errors or wrong interpretations of given an-

swers might occur. In general, both sources of error

(i.e., errors occuring during the interviews and errors

occuring during the transcription) decrease the qual-

ity of the data collected, which further underlines the

need of an electronic support for ﬂexible and mobile

data collection.

In numerous interviews we conducted with 10 do-

main experts from psychology, additional issues have

emerged. Psychological studies are often performed

in developing countries, e.g., surveying of child sol-

diers in rural areas in Africa (Crombach et al., 2013;

Liebrecht, 2012). Political restrictions regarding data

collection further require attention and inﬂuence the

way in which interviews and assessments may be

performed by domain experts (i.e., psychologists).

Since in many geographic regions the available in-

frastructure is not well developed, data collected with

paper-based questionnaires is usually digitalized in

the home country of the scientists responsible for the

study. Taking these issues into account, it is not sur-

prising that psychological studies last from several

weeks up to several months. From a practical point

of view, this raises the problem of allocating enough

space in luggage to transfer the paper-based question-

naires safely to the home country of the respective re-

searcher.

Apart from these logistic problems, we revealed

issues related to the interview procedure itself. In par-

ticular, it has turned out that questionnaires must often

be adapted to a particular application context (e.g.,

changing the language of a questionnaire or adding

/ deleting selected questions). Such adaptations (by

authorized domain experts) must be propagated to all

other interviewers and smart mobile devices respec-

tively in order to keep the results valid and compara-

ble.

Considering these issues, we had additional dis-

cussions with domain experts from psychology,

which revealed several requirements discussed in the

next section.

2.2 Requirements

In the following, we discuss basic requirements for

the mobile support of electronic questionnaires. We

derived these requirements in the context of case stud-

ies, literature analyses, expert interviews, and hands-

on experiences regarding the implementation of mo-

bile data collection applications (Crombach et al.,

2013; Ruf-Leuschner et al., 2013; Isele et al., 2013).

Especially, when interviewing domain experts, fun-

damental requirements could be elicitated. The same

applies to the various paper & pencil questionnaires

we analyzed.

The major requirements are as follows:

R1 (Mobility). The process of collecting data should

be highly ﬂexible and usually requires extensive

interactions. Data may have to be collected even

though no PC is available at the place the ques-

tionnaire should be ﬁlled in. For example, con-

sider data collection at the bedside of a patient in

a hospital or interviews conducted by psycholo-

gists in a meeting room. PCs are often disturbing

in such situations, particularly if the interviewer is

“hiding” himself behind a screen. To enable ﬂexi-

ble data collection, the device needs to be portable

instead. Further, it should not distract the partic-

ipating actors in communicating and interacting

with each other.

R2 (Multi-User Support). Since different users may

interact with a mobile questionnaire, multi-user

support is crucial. In addition, it must be pos-

sible to distinguish between different user roles

(e.g., interviewers and subjects) involved in the

processing of an electronic questionnaire. Finally,

a particular user may possess different roles. For

example, an actor could be interviewer in the con-

text of a speciﬁc questionnaire, but subject in the

context of another one.

R3 (Support of Different Questionnaire Modes).

Generally, a questionnaire may be used in two

different modes: interview and self-rating mode

(cf. Section 2.1). These two modes of question-

ing diverge in the way the questions are posed,

the possible answers that may be given, the or-

der in which the questions are answered, and the

additional features provided (e.g., freetext notes).

In general, mobile electronic questionnaire appli-

cations should allow for both modes. Note, that

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this requirement is correlated with R2 as the con-

sidered roles determine the modes available for a

questionnaire.

R4 (Multi-Language Support). The contents of

a questionnaire (e.g., questions and ﬁeld labels)

may have to be displayed in different languages

(e.g., when conducting a psychological study

globally in different countries). The actor access-

ing the questionnaire should be allowed to choose

among several languages.

R5 (Skeuomorphism and Paper-based Style). To

foster the comprehensibility of an electronic ques-

tionnaire and to ensure its validity, the latter

should be designed in the same style (i.e., same

order and structure) as the corresponding paper-

based version. For example, this means, that the

structuring of a questionnaire in different pages

must be kept.

R6 (Native Application Style). Any mobile support

of electronic questionnaire application must con-

sider different mobile operating systems (e.g., An-

droid or iOS). In this context, standard control el-

ements of the respective mobile operating system

should be used to ensure familiarity of users with

the elements of the questionnaire when running

the latter on their preferred smart mobile device.

R7 (Self-Explaning User Interface). The user in-

terface should be easy to understand and provide

intuitive interaction facilities. Furthermore, users

should be guided through the process of collecting

data with their smart mobile devices.

R8 (Maintainability). Questionnaires evolve over

time and hence may have to be changed occasion-

ally. Therefore, it should be possible to quickly

and easily change the structure and content of an

electronic questionnaire; e.g., to add a question,

to edit the text of a question, to delete a question,

or to change the order of questions. In particular,

no programming skills should be required in this

context; i.e., domain experts (e.g., psychologists)

should be able to introduce respective changes at

a high level of abstraction.

Especially, requirement R8 constitutes a major

challenge, which necessitates a high level of abstrac-

tion when deﬁning and changing electronic question-

naires, which may then be enacted on a variety of

smart mobile devices. To cope with this challenge,

we designed a speciﬁc mental model for electronic

questionnaires, which will be presented in Section 3.

3 MENTAL MODEL

To transfer paper-based questionnaires into electronic

ones and to meet the requirements discussed, we de-

signed a mental model for the support of mobile elec-

tronic questionnaires (cf. Figure 1). According to this

model, the logic of a paper-based questionnaire is de-

scribed in terms of a process model, which is then

deployed to a process management system. The latter

allows creating and executing process (i.e., question-

naire) instances.

Generally, a process model serves as template

for specifying and automating well deﬁned processes

based on process management technology. In addi-

tion, adaptive process management systems allow for

dynamic process changes of instances to handle un-

planned exceptional situations as well (Reichert and

Weber, 2012). In the following, we show that process-

awareness is useful for realizing applications other

than business process automation as well. Applying

the process paradigm and process management tech-

nology in the context of mobile data collection, how-

ever, raises additional challenges (cf. Section 5). We

will show how to realize a process-aware approach

that guides users in ﬁlling in electronic questionnaires

based on process management technology.

3.1 Process Model and Instances

As opposed to traditional information systems,

process-aware information systems (PAIS) separate

process logic from application code. This is accom-

plished based on process models, which provide the

schemes for executing the respective processes (We-

ber et al., 2011). In addition, a process model al-

lows for a visual (i.e., graph-based) representation of

the corresponding process, comprising activities (i.e.,

process steps) as well as the relations (i.e., control and

data ﬂow) between them. For control ﬂow model-

ing, both control edges and gateways (e.g., ANDsplit,

XORsplit) are provided.

A process model P is represented as a directed,

structured graph, which consists of a set of nodes

N (of different types NT ) and directed edges E (of

different types ET ) between them. We assume that

a process model has exactly one start node (NT =

StartFlow) and one end node (NT = EndFlow). Fur-

ther, a process model must be connected; i.e., each

node n can be reached from the start node. In turn,

from any node n of a process model, the end node can

be reached. In this paper, we solely consider block-

structured process models. Each branching (e.g. par-

allel or alternative branching) has exactly one en-

try and one exit node. Further, such blocks may be

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Figure 1: Mental model.

nested, but are not allowed to overlap (Reichert and

Dadam, 2009). In turn, data elements D correspond to

global variables, which are connected with activities

through data ﬂow edges (ET DataFlow). These data

elements can either be read (ReadAccess) or written

(W riteAccess) by an activity (Reichert and Dadam,

1998) from the process model. Figure 3 shows an ex-

ample of such a process model.

In turn, a process instance I represents a concrete

case that is executed based on a process model P. In

general, multiple instances of a process model may

be created and then concurrently executed. Thereby,

the state of an instance is deﬁned by the marking of

its nodes and edges as well as the values of its data

elements. Altogether, respective information corre-

sponds to the execution history of an instance. The

process engine has a set of execution rules which de-

scribe the conditions under which a node may be ac-

tivated (Reichert and Dadam, 1998). If its end node is

reached, a process instance terminates. An example

of how to map a questionnaire to a process model is

provided in Section 3.2.

3.2 Mapping a Questionnaire to a

Process Model

Our mental model enabling a process-driven enact-

ment of questionnaires is as follows: We deﬁne both

the contents and the logic of a questionnaire in terms

of a process model. Thereby, pages of the ques-

tionnaire logically correspond to process activities,

whereas the ﬂow between these activities speciﬁes the

logic of the questionnaire. The questions themselves

are mapped to process data elements, which are con-

nected with the respective activity. There are separate

elements containing the text of a question, which can

be read by the activity. Moreover, there are data el-

ements that can be written by the activity. The latter

are used to store the given answers for a speciﬁc ques-

tion. Figure 2 gives an overview of the mapping of the

elements of a questionnaire to the ones of a process

model.

To illustrate the process-driven modeling of elec-

tronic questionnaires, we present a scenario from psy-

chology. Consider the process-centric questionnaire

model from Figure 3. Its process logic is described in

Figure 2: Mapping a Questionnaire Model to a Process

Model.

terms of BPMN 2.0 (Business Process Model and No-

tation) (Business Process Model, 2011). To establish

the link between process and questionnaire model, we

annotated the depicted graph with additional labels.

The processing of the questionnaire starts with the

execution of activity Page Intro, which presents an in-

troductory text for the participant interacting with the

electronic questionnaire. This introduction includes,

for example, instructions on how to ﬁll in the ques-

tionnaire or how to interact with the smart mobile de-

vice. After completing this ﬁrst step, activity Page

General becomes enabled. In this form-based activ-

ity, data elements Cigarettes, Drugs and Alcohol are

written. More precisely, the values of these data ele-

ments correspond to the answers given for the ques-

tions displayed on the respective page of the ques-

tionnaire. For example, the question corresponding

to data element Cigarettes is as follows: “Do you

smoke?” (with the possible answers “yes / no”). Af-

ter completing activity Page General, an AND gate-

way (ANDsplit) becomes enabled. In turn, all outgo-

ing paths of this ANDsplit (i.e., parallel split node)

become enabled and are then executed concurrently.

In the given application scenario, each of these paths

contains an XOR Gateway (XORsplit), which reads

one of the aforementioned data elements to make a

choice among its outgoing paths. For example, as-

sume that in Page General the participant has an-

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Figure 3: Application Scenario: an abbreviated Questionnaire with Annotations.

swered question “Do you smoke?” with “yes”. Then,

in the respective XOR split, the upper path (labeled

with “yes”) will be chosen, which consists of exactly

one activity, i.e., Page Cigarettes. In the context of

this activity, additional questions regarding the con-

sumption of cigarettes will be displayed to the actor.

This activity and page, respectively, is exemplarily

displayed in Figure 4. Assume further that question

“Do you take drugs? (yes / no)” has been answered

with “no” in the context of Page General. Then, ac-

tivity Page Drugs will be skipped as the lower path

(labeled with “no”) of the respective XOR split will

be chosen. As soon as all three branches are com-

pleted, the ANDjoin will become enabled and the suc-

ceeding activity be displayed. We omit further de-

scriptions for activities of the questionnaire model

due to lack of space. Finally, the processing of a ques-

tionnaire ends with activity Page Outro. Note that an

arbitrary number of questionnaire instances processed

by different participants may be created.

Figure 4 gives an impression of the Page

Cigarettes activity. It displays additional questions

regarding the consumption of cigarettes. This page

is layouted automatically by the electronic ques-

tionnaire application based on the speciﬁed process

model, which includes the pages to be displayed (cf.

Figure 3). Note, that the data elements are used to cre-

ate the user interface, as they contain the actual text of

the questions as well as the possible answers to be dis-

played (i.e., the answers among which the user may

choose).

3.3 Requirements for Process-based

Questionnaires

When using process management technology to coor-

dinate the collection of data with smart mobile de-

Figure 4: Activity “Page Cigarettes”.

vices, additional challenges emerge. In particular,

these are related to the modeling of a questionnaire as

well as the process-driven execution of corresponding

questionnaire instances on smart mobile devices.

Since questionnaire-based interviews are often in-

teractive, the participating roles (e.g., interviewer and

interviewed subject) should be properly assisted when

interacting with the smart mobile device. For ex-

ample, it should be possible for them to start or

abort questionnaire instances. In the context of long-

running questionnaire instances, in addition, it might

be required to interrupt an interview and continue it

later. For this purpose, it must be possible to sus-

pend the execution of a questionnaire instance and to

resume it at a later point in time (with access to all

data and answers collected so far). In the context of

long-running interviews, it is also useful to be able

to display an entire questionnaire and process model

respectively. Therefore, already answered questions

should be displayed differently (e.g., in a different

color) compared to upcoming questions. Note that

this is crucial for providing a quick overview about

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Figure 5: Startable Activities for a Speciﬁc Actor.

the progress of an interview.

Since domain experts might not be familiar with

existing process modeling notations like BPMN 2.0,

an easy-to-understand, self-explaining, and domain-

speciﬁc process notation is needed. In addition, the

roles participating in a questionnaire should be pro-

vided with speciﬁc views on the process model (i.e.,

questionnaire), e.g., hiding information not required

for this role (Kolb and Reichert, 2013). For exam-

ple, a subject may not be allowed to view subsequent

questions in order to ensure credibility of the given

answers.

Regarding the execution of the activities of a ques-

tionnaire (i.e., pages) additional challenges emerge.

The questions of a (psychological) questionnaire

may have to be answered by different actors each of

them possessing a speciﬁc role. For example, follow-

up questions related to the subject involved in a psy-

chological questionnaire might have to be answered

by a psychologist and not by the subject itself. Conse-

quently, the electronic questionnaire application must

ensure that only those questions are displayed to an

actor intended for him or her. Figure 5 shows the

startable activities, currently available for a speciﬁc

actor using the smart mobile device.

In many scenarios, the questions of an electronic

questionnaire may have to be displayed together with

possible answers. In order to avoid bad quality of the

data collected, actors should be further assisted when

interacting with the smart mobile device; e.g., through

error messages, help texts, or on-the-ﬂy validations of

entered data.

To foster the subsequent analysis of the data col-

lected, the latter needs to be archived in a central

repository. Furthermore, additional information (e.g.,

the time it took the subject to answer a particular

question) should be recorded in order to increase

the expressiveness of the data collected. Finally,

anonymization of this data might have to be ensured

as questionnaires often collect personal data and pri-

vacy constitutes a crucial issue. In certain cases, it

might also become neccessary to dismiss the results

of an already answered question.

Taking these general requirements into account,

we designed an architecture for an electronic ques-

tionnaire application.

4 ARCHITECTURE AND

IMPLEMENTATION

This section introduces the architecture we developed

for realizing mobile electronic questionnaires. In par-

ticular, the latter run on smart mobile devices and in-

teract with a remote process engine. This architecture

is presented in Section 4.1. Since this paper focuses

on the requirements, challenges and lessons learned

when applying state-of-the-art process management

technology to realize electronic questionnaires, we

will not describe the architecture of the process man-

agement system (and its process engine) in detail; see

(Dadam and Reichert, 2009; Reichert et al., 2009) for

respective work. The general architecture of our elec-

tronic questionnaire application is depicted in Figure

4.1 Electronic Questionnaire

Application

The electronic questionnaire application is divided

into three main packages, which are related to the user

interface

, the communication

 with the external

process engine, and useful tools for interacting with

the client

.

The user interface representing a particular page

of the questionnaire is represented by an ActivityTem-

plate

, which provides basic methods for the ques-

tionnaire (e.g., to start or stop an activity). In turn,

the LoginView

 is used to query the user creden-

tial and to select an available role for this actor (e.g.,

name = JohnDoe, role = Interviewer). Furthermore,

the MainView

 provides a list (e.g., worklist) with

the pages currently available for the user interacting

with the questionnaire. These list items are repre-

sented using the ProcessAdapter

. Since the user

interface of a questionnaire is generated dynamically

depending on the underlying process model deployed

on the process engine at runtime, a user interface gen-

erator is needed. This service is provided by the Ac-

tivityView

. To interact with the device, different

classes of the Input

 elements used within a ques-

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377

Figure 6: Architecture of the questionnaire application.

tionnaire are provided. These classes provide the nec-

essary logic to interact with the input elements as

well as the corresponding graphical representation of

this element. As certain input elements are platform-

speciﬁc (e.g., there is no spinning wheel for standard

desktop applications), missing input elements might

be rendered differently, depending on the underlying

platform (e.g., the spinning wheel on the iOS platform

could be rendered as a dropdown element on a normal

computer).

The complete communication with the external

process engine should be handled by a Proxy

 ser-

vice. The latter is capable of generating the necessary

request messages, which are then converted to SOAP

request messages by the Communication

 service

and sent to the process engine. The response mes-

sages (e.g., the next page to display) sent by the pro-

cess engine are then received by the Communication

and decomposed by the Proxy. Afterwards, the data

within this message is visualized in the ActivityView,

which includes the already mentioned user interface

generator as well.

4.2 Proof-of-Concept Prototype

To validate the feasibility of the described architec-

ture as well as to be able to apply it in a real setting,

we implemented a proof-of-concept prototype for the

Android platform. We decided to use the latter, since

it is easier to generate and handle SOAP (Curbera

et al., 2002) calls within the application compared

to iOS. The prototype application was then used to

verify the prescribed mental model (cf. Section 3),

and to detail the requirements regarding the execu-

tion of process-aware questionnaires. Furthermore,

Figure 7: Different question types and their visualization

within the questionnaire client application.

additional insights into the practical use of this elec-

tronic questionnaire application by domain experts in

the context of their studies could be gathered. We

were able to meet the requirements presented in Sec-

tion 2.2 when implementing the questionnaire client

application, even though certain drawbacks still exist.

To enable domain experts, who usually have no pro-

gramming skills, to create a mobile electronic ques-

tionnaire, we implemented a fully automated user in-

terface generator for the mobile application itself. In

addition, we were able to provide common types for

questions used within a questionnaire (e.g., likert-

scale, free-text, or yes-no-switches). These types are

automatically mapped to appropriate input elements

visualized within the application. Figure 7 gives an

impression of the input elements implemented.

5 DISCUSSION

This section discusses our approach and reﬂects on

the experiences we gained when applying state-of-

the-art process management technology to support

mobile data collection with electronic questionnaires.

Since we applied an implemented questionnaire in a

psychological study, we were also able to gain addi-

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tional insights into practical issues.

The presented approach has focused on the devel-

opment of smart mobile device applications enabling

ﬂexible data collection rather than on the design of

a development framework. Therefore, we have used

an existing process modeling editor for deﬁning the

process logic of electronic questionnaires. However,

since the domain experts using our questionnaire ap-

plication have been unfamiliar with the BPMN 2.0

modeling notation, a number of training sessions were

required to make them familiar with BPMN 2.0. Af-

terwards, they were able to create their own question-

naires. In particular, the abstraction introduced by the

use of process models for specifying the logic of ques-

tionnaires was well understood by domain experts.

However, the training sessions have shown that there

is a need for a more user-friendly, domain-speciﬁc

modeling notation, enabling domain experts to de-

ﬁne questionnaires on their own. In particular, such a

domain-speciﬁc modeling language needs to be self-

explaining and easy to use. Further, it should hide

modeling elements not required in the given use case.

Note that BPMN 2.0 provides many elements not

needed for deﬁning the logic of electronic question-

naires. Consider, for example, the AND-gateways,

which allow modeling the parallel execution. Regard-

ing the use case of mobile data collection, it does

not matter which path is going to be evaluated ﬁrst.

On the other hand, elements used for modeling the

questionnaire should have a meaningful and expres-

sive representation. Thus, an activity should be rep-

resented as page-symbol to add more context-aware

information to the questionnaire model.

As we further learned in our case study, the cre-

ation and maintenance of a questionnaire constitutes

a highly interactive, ﬂexible and iterative task. In gen-

eral, the editing of already existing, but not yet pub-

lished questionnaires, should be self-explaining. Ba-

sic patterns dealing with the adaptation of the logic of

a questionnaire (e.g., moving a question to another

position or adding a new question) should be inte-

grated in a modeling editor to provide tool-support for

creating and managing questionnaires.

As discussed in Section 3.3, we use process man-

agement technology for modeling and enacting elec-

tronic questionnaires. Accordingly the created ques-

tionnaire model needs to be deployed on a process

engine. Regarding the described client server archi-

tecture (cf. Section 4.1), all process (i.e., question-

naire) models are stored and executed on the server

running the process engine. Keeping in mind that mo-

bile questionnaires might be also used in areas with-

out stable internet connection, any approach requiring

a permanent internet connection between the mobile

client and the process engine running on an external

server will not be accepted. In order to cope with this

issue, a light-weight process engine is required, which

can run on the smart mobile device. We have started

working in this direction as well; e.g., see (Pryss et al.,

2010a; Pryss et al., 2010b).

Since the user interface of the electronic question-

naire is automatically generated based on the pro-

vided process model, the possibilities to customize

the layout of a questionnaire are rather limited. From

the feedback we had obtained from domain experts,

however, it became clear that an expressive layout

component is needed that allows controlling the vi-

sual appearance of a questionnaire running on smart

mobile devices. Among others, different text styles

(e.g., bold), spacing between input elements (e.g.,

bottom spacing), and absolute positioning of elements

should be supported. In addition, the need for inte-

grating images has been expressed several times.

Since we use process-driven electronic question-

naires for collecting data with smart mobile devices,

the answers provided by the actors ﬁlling in the ques-

tionnaire could be directly transferred to the server.

This will relieve the actors from time-consuming

manual tasks. Furthermore, as there exists a pro-

cess model describing the ﬂow logic of the question-

naire as well as comprehensive instance data (e.g., in-

stance execution history), process mining techniques

for analyzing questionnaire instances may be applied

(van der Aalst et al., 2007). In addition, Business In-

telligence Systems (Anandarajan et al., 2003) could

reveal further interesting aspects with respect to the

data collected in order to increase the expressiveness

of the analysis. Such systems would allow for a faster

evaluation and relieve domain experts from manual

tasks such as transferring the collected data into elec-

tronic worksheets.

Finally, we have experienced a strong acceptance

among all participating actors (e.g., interviewers, do-

main experts, and subjects) regarding the practical

beneﬁts of electronic questionnaire applications on

smart mobile devices. Amongst others this was

reﬂected by a much higher willingness to ﬁll out

an electronic questionnaire compared to the respec-

tive paper-based version (Ruf-Leuschner et al., 2013;

Isele et al., 2013). Furthermore, a higher motivation

to complete the questionnaire truthfully could be ob-

served. Of course, this acceptance partly results from

the modern and intuitive way to interact with smart

mobile devices.

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6 RELATED WORK

There exists a variety of questionnaire systems avail-

able on the market. In general, these systems can be

classiﬁed into two groups: online services (Survey-

Monkey, 2013) and standalone applications (Electric

Paper Evaluationssysteme, 2013). Due to the fact that

a questionnaire might contain sensitive information

(e.g., the mental status of a subject or personal de-

tails), online surveys are often not appropriate for this

type of data collection applications. As another limi-

tation of online systems, local authorities do often not

allow third-party software systems to store the infor-

mation of a subject. However, these applications also

must deal with privacy issues. Standalone applica-

tions usually offer possibilities to create a question-

naire, but do not deal with the requirements discussed

in this paper. Furthermore, they lack a ﬂexible and

mobile data collection. Usually, respective question-

naires are displayed as web applications, which can-

not be used when no internet connection is available.

To the best of our knowledge, the process-aware

enactment of questionnaires on smart mobile devices

has not been considered comprehensively by other

groups so far. In previous studies, we identiﬁed

crucial issues regarding the implementation of psy-

chological questionnaires on smart mobile devices

(Liebrecht, 2012; Schindler, 2013). In these stud-

ies, we aimed at preserving the validity of psychologi-

cal instruments, which is a crucial point when replac-

ing paper-based questionnaires with electronic ones.

Although the implemented applications have already

shown several advantages in respect to data collec-

tion and analysis, they have not been fully suitable

for realizing psychological questionnaires in the large

scale yet. In particular, maintenance efforts for do-

main experts and other actors were considerably high.

More precisely, changes of an implemented question-

naire (or its structure) still had to be accomplished by

computer scientists, since its implementation is hard-

coded. Therefore aim at the integration of a process-

aware information system to overcome this limitation.

Focusing on the complete lifecycle of paper-based

questionnaires and supporting every phase with mo-

bile technology has actually not been considered by

other groups so far. However, there exists some work

related to mobile data collection. In particular, mo-

bile process management systems, as described in

(Pryss et al., 2013; Wakholi et al., 2011; Kunze et al.,

2007), could be used to realize electronic question-

naires. However, this use case has not been consid-

ered by respective mobile process engines so far.

The QuON platform (Paul et al., 2013) provides

a web-based survey system, which provides a variety

of different input types for the questions used within

a questionnaire. QuON does not use a model-based

representation to specify a questionnaire as in our ap-

proach. Another distinctive characteristic of QuON is

the webbased-only approach. Especially in psycho-

logical ﬁeld studies, the latter will result in problems

as the QuON platform does not use responsive web-

design.

Movilitas (Movilitas, 2013) applies SAP Sybase

Unwired Platform to enable mobile data collection for

business scenarios. The Sybase Unwired Platform is

a highly adaptive implementation framework for mo-

bile applications, which directly interacts with a back-

end, providing all required business data. Further re-

search is required to show, whether this approach can

be used to realize mobile electronic questionnaires in

domains like psychology or health care as well.

Finally, (Kolb et al., 2012) present a set of pat-

terns for for expressing user interface logic based on

the same notation as used for business process mod-

eling. In particular, a transformation method is intro-

duced, which applies these patterns to automatically

derive user interfaces by establishing a bidirectional

mapping between process model and user interface.

7 SUMMARY & OUTLOOK

In this paper, limitations of paper-based question-

naires for data collection were discussed. To deal

with these limitations, we derived characteristic re-

quirements for electronic questionnaire applications.

In order to meet these requirements, we suggested the

use of process management technology. According to

the mental model introduced, a questionnaire and its

logic can be described in terms of a process model at a

higher level of abstraction. To evaluate our approach,

a sophisticated application scenario from the psycho-

logical domain was considered. We have shown how

a questionnaire can be mapped to a process model.

In the interviews we conducted with domain ex-

perts as well as from other implemented business

applications we elaborated general requirements for

ﬂexible mobile data collection on smart mobile de-

vices. These cover major aspects such as the secure

and encrypted communication. Note that the latter

is crucial, especially in the medical and psychologi-

cal domains, which both deal with sensitive informa-

tion of the subjects involved. We further presented

an architecture enabling such mobile data collection

applications based on a smart mobile device and a

process engine. As another contribution, we demon-

strated the feasibility of our proof-of-concept appli-

cation. Several features as well as problems regard-

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380

ing the implementation and communication with the

server component, hosting the process engine, have

been highlighted. Finally, we discussed the beneﬁts

of using process-aware questionnaire application for

mobile data collection.

In future work, we plan to extend our approach

with additional features. First, we will provide a mo-

bile process engine running on the smart mobile de-

vice itself. This will enable a process-driven enact-

ment of questionnaire instances even if no permanent

internet connection is available. We consider this as a

fundamental feature for enabling ﬂexible data collec-

tion applications on smart mobile devices. However,

this will be accompanied with other problems, such

as the proper synchronization among multiple devices

(e.g., if changes were made to the model of the ques-

tionnaire) in order to keep the devices at the same

level of information. In addition, we want to concep-

tualize a generic questionnaire system, which is able

to support the complete lifecycle of a questionnaire.

To disseminate this system among domain experts be-

ing unfamiliar and unaware of modeling process logic

with standard notations, in addition, an easy to under-

stand, but still precise notation for deﬁning process-

aware questionnaires is needed. To further enhance

data analysis capabilities (e.g., further analysis of the

given answers), we have started to integrate sensors

measuring vital signs in order to gather other informa-

tion about subjects during interviews (Schobel et al.,

2013). As a major beneﬁt of the framework, we ex-

pect higher data quality, shorter evaluation cycles and

a signiﬁcant decrease in workload. In particular, it en-

ables a high level of abstraction in deﬁning electronic

questionnaires that may run on smart mobile devices.

ACKNOWLEDGEMENT

Supported by funds from the program Research initia-

tives, intrastructure, network and transfer plattforms

in the framework of the DFG Excellence Initiative -

Third Funding Line.

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