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
Copyright
c
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
6.
4.1 Electronic Questionnaire
Application
The electronic questionnaire application is divided
into three main packages, which are related to the user
interface
1
, the communication
2
with the external
process engine, and useful tools for interacting with
the client
3
.
The user interface representing a particular page
of the questionnaire is represented by an ActivityTem-
plate
4
, which provides basic methods for the ques-
tionnaire (e.g., to start or stop an activity). In turn,
the LoginView
5
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
6
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
7
. 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
8
. To interact with the device, different
classes of the Input
9
elements used within a ques-
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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
10
ser-
vice. The latter is capable of generating the necessary
request messages, which are then converted to SOAP
request messages by the Communication
11
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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378
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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