Process-Driven Data Collection with Smart Mobile Devices [original]

Process-Driven Data Collection with Smart

Mobile Devices

Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

Institute of Databases and Information Systems, Ulm University, Germany

{johannes.schobel, marc.schickler, ruediger.pryss, manfred.reichert}@uni-ulm.de

Abstract.

Paper-based questionnaires are often used for collecting data

in application domains like healthcare, psychology or education. Such

paper-based approach, however, results in a massive workload for process-

ing and analyzing the collected data. In order to relieve domain experts

from these manual tasks, we propose a process-driven approach for im-

plementing as well as running respective mobile business applications.

In particular, the logic of a questionnaire is described in terms of an

explicit process model. Based on this process model, in turn, multiple

questionnaire instances may be created and enacted by a process engine.

For this purpose, we present a generic architecture and demonstrate

the development of electronic questionnaires in the context of scientific

studies. Further, we discuss the major challenges and lessons learned.

In this context the presented process-driven approach offers promising

perspectives in respect to the development of mobile data collection

applications.

1 Introduction

During the last years smart mobile applications have been increasingly used

in business environment. Examples include applications for task management

and location-based services. In particular, smart mobile devices offer promising

perspectives in respect to mobile data collection as well [13]. For example, data

could be collected with sensors (e.g., pulse sensor), communicating with the smart

mobile device [23] or with form-based end-user applications [12]. Such a mobile

data collection becomes necessary, for example, in the context of clinical trials,

psychological studies, and quality management surveys.

In order to enable mobile data collection, specific knowledge on how to

implement such smart mobile applications is required on one hand. On the other,

domain-specific knowledge is needed, which is usually not available to application

developers. Consequently, costly interactions between domain experts and IT

experts are required. To reduce overall efforts, a framework for rapidly developing

and evolving mobile data collection applications shall be developed. In particular,

respective business applications shall be easy to maintain for non-computer

experts as well. Our overall vision is to enable domain experts to develop mobile

data collection applications themselves at a high level of abstraction. Specifically,

this paper focuses on the process-driven design, implementation and enactment

2 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

of mobile questionnaires that support end users with their daily data collection

tasks.

As application domain for demonstrating the benefits of our approach we

choose psychological studies. Here, domain experts mostly use paper-based

questionnaires for collecting required data from subjects. However, such a paper-

based data collection shows several drawbacks, e.g., regarding the structuring

and layout of a questionnaire (e.g., questions may still be answered even if they

are no longer relevant) as well as the later analysis of answers (e.g., errors might

occur when transferring the paper-based collected data to electronic worksheets).

To cope with these issues and to understand the differences between paper-

based and electronic questionnaires in a mobile context, first of all, we imple-

mented selected questionnaire applications for smart mobile devices and applied

them in real world application settings [3, 21, 6]. In particular, we were able to

demonstrate that mobile electronic questionnaires relieve end users from costly

manual tasks, like the transfer, transformation and analysis of the collected

data. As a major drawback, however, all these applications were hard-coded

and their implementation required considerable interactions with end users. As

a consequence, the respective applications were neither easy to maintain nor

extensible. In order to overcome the gap between the domain-specific design of a

questionnaire and its technical implementation enacted on smart mobile devices,

therefore, an easy to handle, flexible and generic questionnaire system is required.

From the insights we gained during the practical use of the aforementioned

mobile applications as well as from lessons learned in related implemented projects

[20], we elicited the requirements for electronic questionnaire applications that

allow for a flexible mobile 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 psychology

to a process model, which was then deployed to a process engine. In particular,

the process model served as basis for driving the execution of questionnaire

instances at runtime. Note that this mapping allows overcoming many of the

problems known from paper-based questionnaires. In turn, the use of a process

modeling component and a process execution engine in the given context raised

additional challenges. Especially, 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 management technology

may not only be applied in the context of business process automation, but also

provides a promising approach for generating mobile data collection applications.

In particular, a process-driven approach enables end users (i.e., non-computer

experts) to develop mobile electronic questionnaires as well as to deploy them on

smart mobile devices.

The contributions of this paper are as follows:

–

We discuss fundamental problems of paper-based questionnaires and present

requirements regarding their transfer to smart mobile devices.

Process-Driven Data Collection with Smart Mobile Devices 3

–

We provide a mental model for mapping the logic of questionnaires to process

models and illustrate this mental model through a real-world application

scenario from psychology.

–

We present a generic architecture for data collection applications on smart

mobile devices. This architecture can be applied to model, visualize and enact

electronic questionnaires. In particular, it uses process models to define and

control the flow logic of a questionnaire.

–

We share fundamental insights we gathered during the process of implement-

ing and evaluating mobile data collection applications.

The remainder of this paper is organized as follows: Section 2 discusses issues

related to paper-based questionnaires. Further, it elicitates the requirements 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 requirements. In Section 4, we present the basic architecture of

an approach for developing mobile data collection applications. Section 5 provides

a detailed discussion, while Section 6 presents related work. Finally, Section 7

concludes the paper with a summary and outlook.

2 Case Study

In a case study involving 10 domain experts, we analyzed more than 15 paper-

based questionnaires from different domains, including questionnaires used in

the context of psychological studies. Our goal was to understand issues that

emerge when transferring paper-based questionnaires to smart mobile applications.

Section 2.1 discusses basic issues related to paper & pencil questionnaires, while

Section 2.2 elicitates fundamental requirements for their electronic mobile support.

2.1 Paper-Based Questionnaires

We analyzed 15 paper-based questionnaires from psychology 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 statistical evaluations have proven that the results obtained

from the collected data are representive. In addition, the questions are usually

presented in a neutral 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 questionnaire. In particular, reproducible and conclusive results

must be guaranteed. Furthermore, a questionnaire may be used in two different

modes. In the interview mode, the subject is interviewed by a supervisor who

also fills in the questionnaire; i.e., the supervisor 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 questionnaire is

handed out to the subject who then answers the respective questions herself; i.e.,

no supervision is provided in this mode

4 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

Another challenging issue of paper-based questionnaires concerns the analysis

of the data collected. Gathered answers need to be transfered to electronic work-

sheets, which constitutes a time-consuming and error-prone task. For example,

when filling in a questionnaire, typographical errors or wrong interpretations of

given answers might occur. In general, both sources of error (i.e., errors occurring

during the interviews or transcription) decrease the quality of the data collected,

which further underlines the need of an electronic support for data collection.

In numerous interviews we conducted with 10 domain experts from psychology,

additional issues have emerged. Psychological studies are often performed in

developing countries, e.g., surveying of child soldiers in rural areas in Africa

[3]. 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

infrastructure is not well developed, the data collected is usually digitalized in

the home country of the scientists responsible for the study. Taking these issues

into account, it is not surprising 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 questionnaires

safely to the home country of the respective researcher.

Apart from these logistic problems, we revealed issues related to the interview

procedure itself. In particular, it has turned out that questionnaires often need to

be adapted by authorized domain experts to a particular application context (e.g.,

changing the language of a questionnaire or adding / deleting selected questions).

In turn, these adaptations must be propagated to all other interviewers and smart

mobile devices respectively to keep the results valid and comparable.

Considering these issues, we had additional discussions with domain experts,

which revealed several requirements as 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 studies,

literature analyses, expert interviews, and hands-on experiences regarding the

implementation of mobile data collection applications [21, 6]. Especially, when

interviewing domain experts, fundamental requirements could be elicitated. The

same applies to the paper & pencil questionnaires we analyzed.

Basic requirements derived from the interviews are listed below:

R1 (Mobility).

The process of collecting data should be flexible and usually

requires extensive interactions. Data may have to be collected even though

no computer is available at the place the questionnaire should be filled in.

For example, consider data collection at the bedside of a patient in a hospital

or interviews conducted by psychologists in a meeting room. Computers are

often disturbing in such situations, particularly if the interviewer is “hiding”

himself behind a screen. To enable flexible data collection, the device needs

Process-Driven Data Collection with Smart Mobile Devices 5

to be portable instead. Further, it should not distract the participating 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. Furthermore, it must be possible

to distinguish between different user roles (e.g., interviewers and subjects)

involved in the processing of an electronic questionnaire. Note, that a user

may possess different roles. For example, he could be interviewer in the

context of a specific questionnaire, but subject in the context of another one.

R3 (Support of Different Questionnaire Modes).

Generally, a question-

naire may be used in two different modes: interview and self-rating mode (cf.

Section 2.1). These two modes of questioning diverge in the way the questions

are posed, the possible answers that may be given, the order in which the

questions are answered, and the additional features provided (e.g., freetext

notes). In general, mobile electronic questionnaire applications should allow

for both modes. Note, that this requirement is correlated with R2 as the

considered roles determine the modes available for a questionnaire.

R4 (Multi-Language Support).

The contents of a questionnaire (e.g., ques-

tions and field labels) may have to be displayed in different languages (e.g.,

when conducting a psychological study globally). The actor accessing the

questionnaire should be allowed to choose the preferred language.

R5 (Maintainability).

Questionnaires evolve over time and hence may have

to be changed occasionally. 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.

Due to the lack of space, not all requirements are listed here. More re-

quirements with respect to the user interface and native application design are

summarized in [22]. Especially, requirement R5 constitutes a major challenge. It

necessitates a high level of abstraction when defining and changing electronic

questionnaires, which may then be enacted on various 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 designed a mental model for the support of mobile

electronic questionnaires (cf. Figure 1). According to this model, the logic of

a paper-based questionnaire is described in terms of a process model, which is

then deployed to a process management system. The latter allows creating and

executing process (i.e., questionnaire) instances.

Generally, a process model serves as template for specifying, enacting and

evolving structured processes based on process management systems. In addition,

6 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

Process-Aware Information System

Process-aware

Questionnaire

execute

within

Requirements Requirements &

Challenges

Issues with

Paper-Based

Questionnaire

transform deploy

onto results in

Fig. 1. Mental model

adaptive process management systems allow for dynamic changes of process

instances in order to cope with unpredictable situations [19]. In the following, we

show that process models and process management technology are not only useful

in the context of business process automation, but may be applied for mobile

data collection as well. However, this raises additional challenges (cf. Section 5).

This paper will show how to realize a process-aware questionnaire system whose

models guide the users in filling in electronic questionnaires.

3.1 Process Model and Process Instances

As opposed to traditional information systems, process-aware information systems

(PAIS) separate process logic from application code. This is accomplished based

on process models, which provide the schemes for executing the respective

processes [25]. In addition, a process model allows for a visual (i.e., graph-

based) representation of the respective process, comprising the process steps (i.e.,

activities) as well as the relations (i.e., control and data flow) between them.

For control flow modeling, both control edges and gateways (e.g., ANDsplit,

XORsplit) are provided.

A process model

is represented as a directed, structured graph, which

consists of a set of nodes

(of different types

) and directed edges

(of

different types

) between them. We assume that a process model has exactly

one start node (

StartF low

) and one end node (

EndF low

). Further,

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

can be reached from the

start node. In turn, from any node

of a process model, the end node can be

reached. In this paper, we solely consider block-structured process models. Each

branching (e.g. parallel or alternative branching) has exactly one entry and one

exit node. Furthermore, such blocks may be nested, but are not allowed to overlap

[17]. In turn, data elements

correspond to global variables, which are connected

with activities through data flow edges (

ET _DataF low

). These data elements

can either be read (

ReadAccess

) or written (

W riteAccess

) by activities of the

process model [16]. Figure 3 shows an example of a process model.

In turn, a process instance

represents a concrete case that is executed

according to a process model

. In general, for a given process model multiple

instances may be created and concurrently executed. Thereby, the state of a

particular instance is defined by the markings (i.e., states) of its nodes and edges

as well as the values of its data elements. Altogether, respective information

corresponds to the execution history of an instance. Usually, a process engine

relies on a set of execution rules describing the constraints for which a particular

Process-Driven Data Collection with Smart Mobile Devices 7

Questionnaire

Model Page Question

Process

Model

Process

Activity

Process

Data Element

Questionnaire

Instance

Process

Instance

maps to

n 1 1 n n n

n n1 nn 1

maps to

Fig. 2. Mapping a Questionnaire Model to a Process Model

Page

Intro

Page

General ...

Page

Cigarettes

Page

Drugs

Page

Alcohol

Cigarettes Drugs Alcohol Cigarettes

Quantity

Drugs

Quantity

Alcohol

Quantity

StartFlow

Activity

ANDsplit ANDjoinXORsplit XORjoin

DataElement

WriteAccess

ReadAccess

EndFlow

ET_ControlFlow

ET_DataFlow

Page

Outro

yes

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

activity may be activated [16]. If the end node of the process model is reached,

the respective process instance terminates.

3.2 Mapping a Questionnaire to a Process Model

Our mental model for enabling a process-driven enactment of questionnaires is

as follows: We capture both the logic and the content of a questionnaire in a

corresponding process model. Accordingly, pages of the questionnaire logically

correspond to process activities, whereas the flow between these activities specifies

the control flow logic of the questionnaire. The questions themselves are mapped

to process data elements, which are connected 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 elements that can be written by the activity.

The latter are used to store the given answers for a specific question. Figure 2

presents 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 electronic questionnaires, we

present a scenario from psychology. Consider the process-centric questionnaire

model from Figure 3 whose logic is described in terms of BPMN 2.0 (Busi-

ness Process Model and Notation). To establish the link between process and

questionnaire model, we annotate the depicted graph with additional labels.

The processing of the questionnaire starts with the execution of activity Page

Intro, which presents an introductory text to the participant interacting with the

8 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

electronic questionnaire. This introduction includes, for example, instructions on

how to fill in the questionnaire or interact with the smart mobile device. After

completing this first step in the processing of the questionnaire, activity Page

General becomes enabled. In this form-based activity, data elements Cigarettes,

Drugs and Alcohol are written. More precisely, the values of these data elements

correspond to the answers given for the questions displayed on the respective page

of the questionnaire. For example, the question corresponding to data element

Cigarettes is as follows: “Do you smoke?” (with the possible answers “yes / no”).

After completing activity Page General, an AND gateway (ANDsplit) becomes

enabled. In turn, all outgoing 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, assume that in Page General the participant has answered

question “Do you smoke?” with “yes”. Then, in the respective XORsplit, 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 consumption 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 Page General. Then, activity Page Drugs will be skipped as the lower

path (labeled with “no”) of the respective XOR split is chosen. As soon as

all three branches are completed, the ANDjoin will become enabled and the

succeeding activity be displayed. We omit descriptions of the other activities of

the questionnaire model due to lack of space. The processing of a questionnaire

ends with activity Page Outro. Note that, in general, 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 questionnaire application based on the specified

process model, which includes the pages to be displayed (cf. Figure 3). Note that

the user interface is created based on the data elements which contain the text

of the questions as well as the possible answers to be displayed (i.e., the answers

among which the user may choose).

3.3 Requirements for Process-Based Questionnaires

When using process management technology to coordinate the collection of data

with smart mobile devices, additional challenges emerge. In particular, these are

related to the modeling of a questionnaire as well as the process-driven execution

of questionnaire instances on smart mobile devices.

Since questionnaire-based interviews are often interactive, the participating

roles (e.g., interviewer and interviewed subject) should be properly assisted when

interacting with the smart mobile device. For example, 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

Process-Driven Data Collection with Smart Mobile Devices 9

ENTER HERE

Cigarette Consumption

Name your favorite cigarette brand:

How many packs of cigarettes do you smoke within one week?

ok cancel suspend

Do you smoke in your flat?

Fig. 4. Activity “Page Cigarettes” Fig. 5. Startable Activities for an Actor

and continue it later. For this purpose, it must be possible to suspend 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, one must be able to display an entire questionnaire and

process model respectively. Therefore, already answered questions should be

displayed differently (e.g., different color) compared to upcoming questions. Note

that this is crucial for providing a quick overview about the current progress.

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

questionnaire), e.g., hiding information not required for this role [8]. For example,

a subject might not be allowed to view subsequent questions in order to ensure

credibility of the given answers.

Regarding the execution of the activities of a questionnaire (i.e., pages)

additional challenges emerge.

The questions of a (psychological) questionnaire might 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 psychological questionnaire have to

be answered by a psychologist and not by the subject itself. 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. Consequently, the electronic questionnaire

application must ensure that only those questions are displayed to an actor

that are actually intended her. Figure 5 shows the startable activities, currently

available for a specific actor using the smart mobile device.

To foster the subsequent analysis of the data collected, 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 the data might have to be ensured as questionnaires often collect personal data

10 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

Electronic Questionnaire Application

User Interface

ActivityTemplate LoginView

MainViewActivityView

Communication

Proxy Communication

ConfigurationXMLGenerator

Tools

Input InputTypes ProcessAdapter

Remote Process Engine

SOAP

4 5

9 7

10 11

Fig. 6. Architecture of the Electronic Questionnaire Application

and privacy constitutes a crucial issue. In certain cases, it might also become

necessary to dismiss the results of an already answered question.

Taking these general requirements into account, we designed an architecture

for an electronic questionnaire application.

4 Architecture and Implementation

This section introduces the architecture we developed for realizing electronic

questionnaires. In particular, the latter run on smart mobile devices and interact

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 remote process man-

agement system in detail (see [4, 18] for respective work). The general architecture

of our electronic 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



, the communication



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 ActivityTemplate



, which provides basic methods for the

questionnaire (e.g., to start or stop an activity). In turn, the LoginView



is used

to query the user credential 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. The list items are represented using the ProcessAdapter



. Since the user interface of a questionnaire is generated dynamically depending

on the underlying process model that has been deployed on the process engine, a

user interface generator is needed. This service is provided by the ActivityView



To interact with the device, different classes of the Input



elements used within

a questionnaire are provided. These classes provide the necessary logic to interact

Process-Driven Data Collection with Smart Mobile Devices 11

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 iOS could be rendered as a dropdown element on a normal computer).

The entire communication with the external process engine should be handled

by a Proxy



service. The latter is capable of generating the necessary request

messages, which are then converted to SOAP request messages by the Commu-

nication



service and sent to the process engine. The response messages (e.g.,

the next page to display) sent by the process 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 architecture as well as to apply it in

a real setting, we implemented a proof-of-concept prototype for Android. The

prototype application was then used to verify the prescribed mental model, and to

detail the requirements regarding the execution of process-aware questionnaires.

Furthermore, additional insights into the practical use of this application by

domain experts in the context of their studies could be gathered. We were

able to meet the requirements presented in Section 2.2 when implementing the

questionnaire client application, even though certain drawbacks still exist. In

order to enable domain experts, who usually have no programming skills, to

create a mobile electronic questionnaire, we implemented a fully automated

user interface 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.

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 insights into

practical issues as well.

The presented approach has focused on the development of mobile business

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 logic of electronic questionnaires. However, since the

domain experts who have been using our questionnaire application have been

unfamiliar with the BPMN 2.0 modeling notation, a number of training sessions

were required. Afterwards, they were able to create their own questionnaires.

12 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

In particular, the abstraction introduced by the use of process models for

specifying questionnaire logic 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 define question-

naires 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 questionnaires. Consider,

for example, the AND gateways, which allow modeling parallel execution paths.

Regarding the use case of mobile data collection, it does not matter which path is

going to be evaluated first. In addition, the elements for modeling a questionnaire

should have a clear semantics and be expressive enough. Therefore, an activity

should be represented as page-symbol to add context-aware information to the

questionnaire model.

As we further learned in our case study, the creation and maintenance of

a questionnaire constitutes a highly interactive, flexible and iterative task. In

general, the editing of already existing, but not yet published questionnaires,

should be self-explaining. Basic 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 integrated in a modeling editor to provide tool support for

creating and managing questionnaires.

As discussed in Section 3.3, we use process management technology for

both modeling and enacting electronic questionnaires. Accordingly, the created

questionnaire model needs to be deployed on a process engine. Regarding the

described client server architecture (cf. Section 4.1), all process (i.e., questionnaire)

models are stored and executed on the server running the process engine. Keeping

in mind that mobile questionnaires might be also used in areas without 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 [14, 15]).

Since the user interface of the electronic questionnaire is automatically gen-

erated based on the provided 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 compo-

nent is needed that allows controlling the visual 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 considered. In addition, the need for integrating images

has been expressed several times.

Since we use process-driven electronic questionnaires for collecting data

with smart mobile devices, the answers provided by the actors filling in the

questionnaire could be directly transferred to the server. This will relieve the

actors from time-consuming manual tasks. Furthermore, as there exists a process

Process-Driven Data Collection with Smart Mobile Devices 13

model describing the flow logic of the questionnaire as well as comprehensive

instance data (e.g., instance execution history), process mining techniques for

analyzing questionnaire instances might be applied [1]. In addition, Business

Intelligence Systems [2] 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 electronic worksheets.

Finally, we have experienced a strong acceptance among all participating

actors (e.g., interviewers, domain 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 respective paper-based version [21, 6]. Furthermore,

a higher motivation to complete the questionnaire truthfully could be observed.

Of course, this acceptance partly results from the modern and intuitive way to

interact with smart mobile devices.

6 Related Work

There exists a variety of questionnaire systems available on the market. In general,

these systems can be classified into two groups: online services [24] and standalone

applications [5]. Due to the fact that a questionnaire might contain sensitive

information (e.g., the mental status of a subject or personal details), online

surveys are often not appropriate for this type of data collection applications.

As another limitation of online systems, local authorities do often not allow

third-party software systems to store the information of a subject. However,

these applications also must deal with privacy issues. Standalone applications

usually offer possibilities to create a questionnaire, but do not deal with the

requirements discussed in this paper. Furthermore, they lack a flexible and

mobile data collection. Usually, respective questionnaires are displayed as web

applications, which cannot 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 psychological questionnaires on smart mobile devices [3, 6, 21]. In these studies,

we aimed at preserving the validity of psychological instruments, which is a

crucial point when replacing paper-based questionnaires with electronic ones.

Although the implemented applications have already shown several advantages

in respect to data collection and analysis, they have not been fully suitable

for realizing psychological questionnaires in the large scale yet. In particular,

maintenance efforts for domain experts and other actors were considerably high.

More precisely, changes of an implemented questionnaire (or its structure) still had

to be accomplished by computer experts, due to its implementation. Therefore, we

aim to integrate a process-aware information system to overcome this limitation.

Focusing on the complete lifecycle of paper-based questionnaires and sup-

porting every phase with mobile technology has actually not been considered by

14 Johannes Schobel, Marc Schickler, Rüdiger Pryss, and Manfred Reichert

other groups so far. However, there exists related work regarding mobile data

collection. In particular, mobile process management systems, as described in

[13, 9], could be used to realize electronic questionnaires. However, this use case

has not been considered by existing mobile process engines so far.

The QuON platform [11] is a web-based survey system, which provides a

variety of different input types for the questions of a questionnaire. As opposed to

our approach, QuON does not apply a model-based representation for specifying

a questionnaire. Another limitation of QuON is its restriction to web-based

questionnaires. Especially, in psychological field studies, the latter will result in

problems as the QuON platform does not use responsive webdesign.

Movilitas applies SAP Sybase Unwired Platform to enable a mobile data

collection for business scenarios [10]. In turn, the Sybase Unwired Platform

constitutes a highly adaptive implementation framework for mobile applications,

which interacts with a backend, that provides all relevant business data. Further

research is required to show whether this approach can be applied to realize

mobile electronic questionnaires in domains like psychology or healthcare as well.

Finally, [7] presents a set of patterns for expressing user interface logic based

on the same notation as used for process modeling. In particular, a transformation

method applies these patterns to automatically derive user interfaces based on a

bidirectional mapping between process model and user interface.

7 Summary and Outlook

In this paper, limitations of paper-based questionnaires for data collection were

discussed. To deal with these limitations, we derived characteristic requirements

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 psychological domain was considered.

We have shown how a questionnaire can be mapped to a process model.

In the interviews we conducted with domain experts as well as from other

mobile business applications we implemented, general requirements for flexible

mobile data collection on smart mobile devices were elaborated. These cover

major aspects like a secure and encrypted communication. Note that the latter

is crucial, especially in domains like medicine and psychology, which both deal

with sensitive information of the subjects involved. We further presented an

architecture enabling mobile data collection applications based on a smart mobile

device and a process engine. As another contribution, we demonstrated the

feasibility of our proof-of-concept application. Several features as well as problems

regarding the implementation and communication with the server component,

hosting the process engine, have been highlighted. Finally, we discussed the

benefits of using process-driven questionnaires for mobile data collection.

In future work, we plan to extend our approach with additional features.

Currently, we are working on a mobile process engine running on the smart mobile

Process-Driven Data Collection with Smart Mobile Devices 15

device. In turn, this will enable a process-driven enactment of questionnaire

instances, even if no Internet connection is available. We consider this as a

fundamental feature for enabling flexible data collection applications on smart

mobile devices. A first prototype is already implemented and integrated into

the presented electronic questionnaire application. However, this will cause new

problems, as the questionnaire models must be properly synchronized among

multiple devices (e.g., if changes were made to the questionnaire model). In

addition, we are working on a questionnaire modeling notation. This is crucial

to allow domain experts, who are unfamiliar with standard process modeling

notations. This domain-specific questionnaire modeling notation shall be easy to

understand, but still precise enough for defining process-aware questionnaires.

The notation will be part of a generic questionnaire system supporting the

complete lifecycle of a questionnaire. Furthermore, we started to integrate sensors

measuring vital signs in order to gather other information about subjects during

interviews [23]. As a major benefit of the framework, we expect higher data

quality, shorter evaluation cycles and a significant decrease of workloads.

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