Towards Process-Driven Mobile Data Collection Applications:Requirements, Challenges, Lessons Learned

Johannes Schobel, Marc Schickler, Rudiger Pryss, Fabian Maier, and Manfred ReichertInstitute of Databases and Information Systems, University of Ulm, James-Franck-Ring, Ulm, Germany{johannes.schobel, marc.schickler, ruediger.pryss, fabian.maier, manfred.reichert}@uni-ulm.de

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 specificallytailored paper & pencil questionnaires. Usually, such a paper-based data collection is accomplished by amassive workload regarding the processing, analysis, and evaluation of the data collected. To relieve domainexperts from these manual tasks and to increase the efficiency of the data collection process, we developed ageneric approach for realizing process-driven smart mobile device applications based on process managementtechnology. According to this approach, the logic of a questionnaire is described in terms of an explicitprocess model whose enactment is driven by a generic process engine. Our goal is to demonstrate that such aprocess-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 psychologicalstudies. 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 ata 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 (Prysset al., 2013). Thereby, data can be collected withsensors (e.g., pulse sensor), communicating with thesmart mobile device (Schobel et al., 2013), or withsmart form-based applications (Pryss et al., 2012).Examples of applications requiring such a mobile datacollection include clinical trials, psychological stud-ies, and quality management surveys.

Developing a mobile data collection applicationrequires specific knowledge on how to implementsmart mobile applications. Furthermore, it requiresdomain-specific knowledge, usually not available tothe programmers of these applications. Hence, toavoid a gap between business needs and IT solutions,continuous and costly communication between do-main and IT experts becomes necessary. To improvethis situation, a framework for rapidly developing andevolving 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 toenable 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 expertswith 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-basedquestionnaires for collecting required data from sub-jects. However, such a paper-based data collectionshows several drawbacks, e.g., regarding the struc-ture and layout of a questionnaire (e.g., questions maystill 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 thesubtle differences between paper-based and electronicquestionnaires in a mobile context, first of all, weimplemented several questionnaire applications forsmart mobile devices and applied them in real and

sophisticated application settings (Liebrecht, 2012;Schindler, 2013). In particular, we were able todemonstrate 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 hadimplemented were hard-coded and required consid-erable communication with domain experts. As aconsequence, these applications were neither easy tomaintain nor extensible. However, in order to avoida gap between the domain-specific design of a ques-tionnaire and its technical implementation enacted onsmart mobile devices, an easy to handle, flexible andgeneric questionnaire system is indispensable.

From the insights we gained during the practi-cal use of the above mentioned mobile applicationsas well as from lessons learned when implementingother kinds of mobile applications (Robecke et al.,2011), we elicited the requirements for electronicquestionnaire applications that enable a flexible mo-bile data collection. In order to evaluate whether theuse of process management technology contributesto the satisfaction of these requirements, we mappedthe 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 theexecution of questionnaire instances at realtime. Inparticular, this mapping allows us to overcome manyof 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 givencontext, raised additional challenges, e.g., related tothe process-driven execution of electronic question-naires on and the mobile data collection with smartmobile devices. The implemented questionnaire runson a mobile device and communicates with a remoteprocess 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 providesa promising approach for generating mobile data col-lection applications. In particular, a process-drivenapproach enables non-computer experts to developelectronic questionnaires for smart mobile devices aswell as to deploy them on respective devices in orderto collect data with them.

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

• We discuss fundamental problems of paper-basedquestionnaires 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 illustratethis mental model through a real-world applica-

tion scenario from the psychological domain.

• We present a generic architecture for applicationsrunning on smart mobile devices that can be usedto model, visualize and enact electronic question-naires. This approach relies on the provided men-tal model and uses process models to define andcontrol the flow logic of a questionnaire.

• We share fundamental insights we gathered dur-ing the process of implementing and evaluatingthe 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 runningon smart mobile devices. Section 3 describes themental model we suggest for meeting these require-ments. In Section 4, we present the basic architectureof our approach for developing mobile data collectionapplications. 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 analyzedmore than 15 paper-based questionnaires from dif-ferent domains, particularly questionnaires used inthe context of psychological studies. Our goal wasto 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, Section2.2 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 ofissues emerged. First, in the considered domains, aquestionnaire must be valid. This means that it shouldhave already been applied in several studies, and sta-tistical evaluations have proven that the results ob-tained from the collected data are representive. Inaddition, 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 ofthe main goals when setting up a psychological ques-tionnaire. In particular, reproducible and conclusive

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 orskip. This mode usually requires a lot of experiencesince the interviewer must also deal with questionsthat might be critical for the subject. The other modewe consider is self-rating. In this mode, the question-naire is handed out to the subject who then answersthe respective questions herself; i.e., no supervision isprovided 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 electronicworksheets, which constitutes a time-consuming anderror-prone task. In particular, note that during theinterviews 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 errorsoccuring during the transcription) decrease the qual-ity of the data collected, which further underlines theneed of an electronic support for flexible and mobiledata collection.

In numerous interviews we conducted with 10 do-main experts from psychology, additional issues haveemerged. Psychological studies are often performedin developing countries, e.g., surveying of child sol-diers in rural areas in Africa (Crombach et al., 2013;Liebrecht, 2012). Political restrictions regarding datacollection further require attention and influence theway in which interviews and assessments may beperformed by domain experts (i.e., psychologists).Since in many geographic regions the available in-frastructure is not well developed, data collected withpaper-based questionnaires is usually digitalized inthe home country of the scientists responsible for thestudy. Taking these issues into account, it is not sur-prising that psychological studies last from severalweeks up to several months. From a practical pointof view, this raises the problem of allocating enoughspace 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 revealedissues related to the interview procedure itself. In par-ticular, it has turned out that questionnaires must oftenbe adapted to a particular application context (e.g.,changing the language of a questionnaire or adding/ deleting selected questions). Such adaptations (byauthorized domain experts) must be propagated to allother 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 thenext section.

2.2 Requirements

In the following, we discuss basic requirements forthe mobile support of electronic questionnaires. Wederived 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 sameapplies to the various paper & pencil questionnaireswe analyzed.

The major requirements are as follows:R1 (Mobility). The process of collecting data should

be highly flexible and usually requires extensiveinteractions. Data may have to be collected eventhough 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 ina hospital or interviews conducted by psycholo-gists in a meeting room. PCs are often disturbingin such situations, particularly if the interviewer is“hiding” himself behind a screen. To enable flexi-ble data collection, the device needs to be portableinstead. Further, it should not distract the partic-ipating actors in communicating and interactingwith each other.

R2 (Multi-User Support). Since different users mayinteract with a mobile questionnaire, multi-usersupport is crucial. In addition, it must be pos-sible to distinguish between different user roles(e.g., interviewers and subjects) involved in theprocessing of an electronic questionnaire. Finally,a particular user may possess different roles. Forexample, an actor could be interviewer in the con-text of a specific questionnaire, but subject in thecontext of another one.

R3 (Support of Different Questionnaire Modes).Generally, a questionnaire may be used in twodifferent 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 theadditional features provided (e.g., freetext notes).In general, mobile electronic questionnaire appli-cations should allow for both modes. Note, that

this requirement is correlated with R2 as the con-sidered roles determine the modes available for aquestionnaire.

R4 (Multi-Language Support). The contents ofa questionnaire (e.g., questions and field labels)may have to be displayed in different languages(e.g., when conducting a psychological studyglobally in different countries). The actor access-ing the questionnaire should be allowed to chooseamong several languages.

R5 (Skeuomorphism and Paper-based Style). Tofoster the comprehensibility of an electronic ques-tionnaire and to ensure its validity, the lattershould be designed in the same style (i.e., sameorder and structure) as the corresponding paper-based version. For example, this means, that thestructuring of a questionnaire in different pagesmust be kept.

R6 (Native Application Style). Any mobile supportof 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 systemshould be used to ensure familiarity of users withthe elements of the questionnaire when runningthe latter on their preferred smart mobile device.

R7 (Self-Explaning User Interface). The user in-terface should be easy to understand and provideintuitive interaction facilities. Furthermore, usersshould be guided through the process of collectingdata with their smart mobile devices.

R8 (Maintainability). Questionnaires evolve overtime and hence may have to be changed occasion-ally. Therefore, it should be possible to quicklyand easily change the structure and content of anelectronic 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 thiscontext; i.e., domain experts (e.g., psychologists)should be able to introduce respective changes ata high level of abstraction.

Especially, requirement R8 constitutes a majorchallenge, which necessitates a high level of abstrac-tion when defining and changing electronic question-naires, which may then be enacted on a variety ofsmart mobile devices. To cope with this challenge,we designed a specific mental model for electronicquestionnaires, which will be presented in Section 3.

3 MENTAL MODEL

To transfer paper-based questionnaires into electronicones 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 thismodel, the logic of a paper-based questionnaire is de-scribed in terms of a process model, which is thendeployed to a process management system. The latterallows creating and executing process (i.e., question-naire) instances.

Generally, a process model serves as templatefor specifying and automating well defined processesbased on process management technology. In addi-tion, adaptive process management systems allow fordynamic process changes of instances to handle un-planned exceptional situations as well (Reichert andWeber, 2012). In the following, we show that process-awareness is useful for realizing applications otherthan business process automation as well. Applyingthe process paradigm and process management tech-nology in the context of mobile data collection, how-ever, raises additional challenges (cf. Section 5). Wewill show how to realize a process-aware approachthat guides users in filling in electronic questionnairesbased on process management technology.

3.1 Process Model and Instances

As opposed to traditional information systems,process-aware information systems (PAIS) separateprocess logic from application code. This is accom-plished based on process models, which provide theschemes 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 ofthe corresponding process, comprising activities (i.e.,process steps) as well as the relations (i.e., control anddata 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 nodesN (of different types NT ) and directed edges E (ofdifferent types ET ) between them. We assume thata 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., eachnode n can be reached from the start node. In turn,from any node n of a process model, the end node canbe 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

Process-Aware Information System

Process-aware

Questionnaire

execute

within

Requirements Requirements &

ChallengesIssues with

Paper-Based

Questionnaire

transformdeploy

ontoresults in

A

B

A

B

A

B

A

B

A

B

A

B

Figure 1: Mental model.

nested, but are not allowed to overlap (Reichert andDadam, 2009). In turn, data elements D correspond toglobal variables, which are connected with activitiesthrough data flow edges (ET DataFlow). These dataelements can either be read (ReadAccess) or written(WriteAccess) 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 concretecase that is executed based on a process model P. Ingeneral, multiple instances of a process model maybe created and then concurrently executed. Thereby,the state of an instance is defined by the marking ofits nodes and edges as well as the values of its dataelements. Altogether, respective information corre-sponds to the execution history of an instance. Theprocess 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 isreached, a process instance terminates. An exampleof how to map a questionnaire to a process model isprovided in Section 3.2.

3.2 Mapping a Questionnaire to aProcess Model

Our mental model enabling a process-driven enact-ment of questionnaires is as follows: We define boththe contents and the logic of a questionnaire in termsof a process model. Thereby, pages of the ques-tionnaire logically correspond to process activities,whereas the flow between these activities specifies thelogic of the questionnaire. The questions themselvesare mapped to process data elements, which are con-nected with the respective activity. There are separateelements containing the text of a question, which canbe read by the activity. Moreover, there are data el-ements that can be written by the activity. The latterare used to store the given answers for a specific ques-tion. Figure 2 gives an overview of the mapping of theelements of a questionnaire to the ones of a processmodel.

To illustrate the process-driven modeling of elec-tronic questionnaires, we present a scenario from psy-chology. Consider the process-centric questionnairemodel from Figure 3. Its process logic is described in

Questionnaire

Model

Page

Question

Process

Model

Activity

Data Element

1

n

n

n

n

n

1

n

Questionnaire

Instance

Process

Instance

n

1

n

1

represented as

represented as

represented as

represented as

Figure 2: Mapping a Questionnaire Model to a ProcessModel.

terms of BPMN 2.0 (Business Process Model and No-tation) (Business Process Model, 2011). To establishthe link between process and questionnaire model, weannotated the depicted graph with additional labels.

The processing of the questionnaire starts with theexecution of activity Page Intro, which presents an in-troductory text for the participant interacting with theelectronic 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 PageGeneral becomes enabled. In this form-based activ-ity, data elements Cigarettes, Drugs and Alcohol arewritten. 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 correspondingto data element Cigarettes is as follows: “Do yousmoke?” (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 pathscontains an XOR Gateway (XORsplit), which readsone of the aforementioned data elements to make achoice among its outgoing paths. For example, as-

Page

Intro

Page

General...

Page

Cigarettes

Page

Drugs

Page

Alcohol

Cigarettes Drugs AlcoholCigarettes

Quantity

Drugs

Quantity

Alcohol

Quantity

StartFlow

Activity

ANDsplit ANDjoin

XORsplit XORjoin

DataElement

WriteAccess

ReadAccess

EndFlow

ET_ControlFlow

ET_DataFlow

Page

Outro

yes

no

yes

yes

no

no

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

sume that in Page General the participant has an-swered question “Do you smoke?” with “yes”. Then,in the respective XOR split, the upper path (labeledwith “yes”) will be chosen, which consists of exactlyone activity, i.e., Page Cigarettes. In the context ofthis activity, additional questions regarding the con-sumption of cigarettes will be displayed to the actor.This activity and page, respectively, is exemplarilydisplayed in Figure 4. Assume further that question“Do you take drugs? (yes / no)” has been answeredwith “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 willbe 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 modeldue to lack of space. Finally, the processing of a ques-tionnaire ends with activity Page Outro. Note that anarbitrary number of questionnaire instances processedby different participants may be created.

Figure 4 gives an impression of the PageCigarettes activity. It displays additional questionsregarding the consumption of cigarettes. This pageis layouted automatically by the electronic ques-tionnaire application based on the specified processmodel, 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 ofthe questions as well as the possible answers to be dis-played (i.e., the answers among which the user maychoose).

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?

No

Figure 4: Activity “Page Cigarettes”.

3.3 Requirements for Process-BasedQuestionnaires

When using process management technology to coor-dinate the collection of data with smart mobile de-vices, additional challenges emerge. In particular,these are related to the modeling of a questionnaire aswell as the process-driven execution of correspondingquestionnaire instances on smart mobile devices.

Since questionnaire-based interviews are often in-teractive, the participating roles (e.g., interviewer andinterviewed subject) should be properly assisted wheninteracting with the smart mobile device. For ex-ample, it should be possible for them to start orabort questionnaire instances. In the context of long-running questionnaire instances, in addition, it mightbe required to interrupt an interview and continue itlater. For this purpose, it must be possible to sus-pend the execution of a questionnaire instance and toresume it at a later point in time (with access to alldata and answers collected so far). In the context of

Figure 5: Startable Activities for a Specific Actor.

long-running interviews, it is also useful to be ableto display an entire questionnaire and process modelrespectively. Therefore, already answered questionsshould be displayed differently (e.g., in a differentcolor) compared to upcoming questions. Note thatthis is crucial for providing a quick overview aboutthe progress of an interview.

Since domain experts might not be familiar withexisting process modeling notations like BPMN 2.0,an easy-to-understand, self-explaining, and domain-specific process notation is needed. In addition, theroles participating in a questionnaire should be pro-vided with specific views on the process model (i.e.,questionnaire), e.g., hiding information not requiredfor this role (Kolb and Reichert, 2013). For exam-ple, a subject may not be allowed to view subsequentquestions in order to ensure credibility of the givenanswers.

Regarding the execution of the activities of a ques-tionnaire (i.e., pages) additional challenges emerge.

The questions of a (psychological) questionnairemay have to be answered by different actors each ofthem possessing a specific role. For example, follow-up questions related to the subject involved in a psy-chological questionnaire might have to be answeredby a psychologist and not by the subject itself. Conse-quently, the electronic questionnaire application mustensure that only those questions are displayed to anactor intended for him or her. Figure 5 shows thestartable activities, currently available for a specificactor using the smart mobile device.

In many scenarios, the questions of an electronicquestionnaire may have to be displayed together withpossible answers. In order to avoid bad quality of thedata collected, actors should be further assisted wheninteracting with the smart mobile device; e.g., througherror messages, help texts, or on-the-fly validations ofentered data.

To foster the subsequent analysis of the data col-lected, the latter needs to be archived in a centralrepository. Furthermore, additional information (e.g.,the time it took the subject to answer a particularquestion) should be recorded in order to increasethe expressiveness of the data collected. Finally,anonymization of this data might have to be ensuredas questionnaires often collect personal data and pri-vacy constitutes a crucial issue. In certain cases, itmight also become neccessary to dismiss the resultsof an already answered question.

Taking these general requirements into account,we designed an architecture for an electronic ques-tionnaire application.

4 ARCHITECTURE ANDIMPLEMENTATION

This section introduces the architecture we developedfor realizing mobile electronic questionnaires. In par-ticular, the latter run on smart mobile devices and in-teract with a remote process engine. This architectureis presented in Section 4.1. Since this paper focuseson the requirements, challenges and lessons learnedwhen applying state-of-the-art process managementtechnology to realize electronic questionnaires, wewill 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) forrespective work. The general architecture of our elec-tronic questionnaire application is depicted in Figure6.

4.1 Electronic QuestionnaireApplication

The electronic questionnaire application is dividedinto three main packages, which are related to the userinterface 1©, the communication 2© with the externalprocess engine, and useful tools for interacting withthe client 3©.

The user interface representing a particular pageof 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) withthe pages currently available for the user interactingwith the questionnaire. These list items are repre-sented using the ProcessAdapter 7©. Since the user

Electronic Questionnaire Application

User Interface

ActivityTemplate LoginView

MainView

ActivityView

Communication

Proxy Communication

Configuration

Responses

XMLGenerator

Namespaces

Tools

Input InputBoolean

InputString

InputScaleInputDate

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cess

En

gin

e (

Ari

sta

Flo

w)

SOAP

InputInteger

1

3

2

4 5

6

8

9 7

10 11

Figure 6: Architecture of the questionnaire application.

interface of a questionnaire is generated dynamicallydepending on the underlying process model deployedon 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, differentclasses of the Input 9© elements used within a ques-tionnaire are provided. These classes provide the nec-essary logic to interact with the input elements aswell as the corresponding graphical representation ofthis element. As certain input elements are platform-specific (e.g., there is no spinning wheel for standarddesktop applications), missing input elements mightbe rendered differently, depending on the underlyingplatform (e.g., the spinning wheel on the iOS platformcould be rendered as a dropdown element on a normalcomputer).

The complete communication with the externalprocess engine should be handled by a Proxy 10© ser-vice. The latter is capable of generating the necessaryrequest messages, which are then converted to SOAPrequest messages by the Communication 11© serviceand 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 Communicationand decomposed by the Proxy. Afterwards, the datawithin this message is visualized in the ActivityView,which includes the already mentioned user interfacegenerator 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

Figure 7: Different question types and their visualizationwithin the questionnaire client application.

Android platform. We decided to use the latter, sinceit is easier to generate and handle SOAP (Curberaet al., 2002) calls within the application comparedto iOS. The prototype application was then used toverify the prescribed mental model (cf. Section 3),and to detail the requirements regarding the execu-tion of process-aware questionnaires. Furthermore,additional insights into the practical use of this elec-tronic questionnaire application by domain experts inthe context of their studies could be gathered. Wewere able to meet the requirements presented in Sec-tion 2.2 when implementing the questionnaire clientapplication, 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. Inaddition, we were able to provide common types forquestions used within a questionnaire (e.g., likert-scale, free-text, or yes-no-switches). These types areautomatically mapped to appropriate input elementsvisualized within the application. Figure 7 gives animpression of the input elements implemented.

5 DISCUSSION

This section discusses our approach and reflects onthe experiences we gained when applying state-of-the-art process management technology to supportmobile data collection with electronic questionnaires.Since we applied an implemented questionnaire in apsychological study, we were also able to gain addi-tional insights into practical issues.

The presented approach has focused on the devel-opment of smart mobile device applications enablingflexible data collection rather than on the design ofa development framework. Therefore, we have usedan existing process modeling editor for defining theprocess logic of electronic questionnaires. However,since the domain experts using our questionnaire ap-plication have been unfamiliar with the BPMN 2.0modeling notation, a number of training sessions wererequired 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 theuse of process models for specifying the logic of ques-tionnaires was well understood by domain experts.However, the training sessions have shown that thereis a need for a more user-friendly, domain-specificmodeling notation, enabling domain experts to de-fine questionnaires on their own. In particular, such adomain-specific modeling language needs to be self-explaining and easy to use. Further, it should hidemodeling elements not required in the given use case.Note that BPMN 2.0 provides many elements notneeded 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 doesnot matter which path is going to be evaluated first.On the other hand, elements used for modeling thequestionnaire should have a meaningful and expres-sive representation. Thus, an activity should be rep-resented as page-symbol to add more context-awareinformation to the questionnaire model.

As we further learned in our case study, the cre-ation and maintenance of a questionnaire constitutesa 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 ofa questionnaire (e.g., moving a question to anotherposition or adding a new question) should be inte-grated in a modeling editor to provide tool-support forcreating 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 processengine. Regarding the described client server archi-tecture (cf. Section 4.1), all process (i.e., question-naire) models are stored and executed on the serverrunning the process engine. Keeping in mind that mo-bile questionnaires might be also used in areas with-out stable internet connection, any approach requiringa permanent internet connection between the mobileclient and the process engine running on an externalserver will not be accepted. In order to cope with thisissue, a light-weight process engine is required, whichcan run on the smart mobile device. We have startedworking 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 customizethe layout of a questionnaire are rather limited. Fromthe feedback we had obtained from domain experts,however, it became clear that an expressive layoutcomponent is needed that allows controlling the vi-sual appearance of a questionnaire running on smartmobile devices. Among others, different text styles(e.g., bold), spacing between input elements (e.g.,bottom spacing), and absolute positioning of elementsshould 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-consumingmanual 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 techniquesfor analyzing questionnaire instances may be applied(van der Aalst et al., 2007). In addition, Business In-telligence Systems (Anandarajan et al., 2003) couldreveal further interesting aspects with respect to thedata collected in order to increase the expressivenessof the analysis. Such systems would allow for a fasterevaluation and relieve domain experts from manualtasks such as transferring the collected data into elec-tronic worksheets.

Finally, we have experienced a strong acceptanceamong all participating actors (e.g., interviewers, do-main experts, and subjects) regarding the practicalbenefits of electronic questionnaire applications onsmart mobile devices. Amongst others this wasreflected by a much higher willingness to fill outan 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 fromthe modern and intuitive way to interact with smartmobile devices.

6 RELATED WORK

There exists a variety of questionnaire systems avail-able on the market. In general, these systems can beclassified into two groups: online services (Survey-Monkey, 2013) and standalone applications (ElectricPaper Evaluationssysteme, 2013). Due to the fact thata questionnaire might contain sensitive information(e.g., the mental status of a subject or personal de-tails), online surveys are often not appropriate for thistype of data collection applications. As another limi-tation of online systems, local authorities do often notallow third-party software systems to store the infor-mation of a subject. However, these applications alsomust deal with privacy issues. Standalone applica-tions usually offer possibilities to create a question-naire, but do not deal with the requirements discussedin this paper. Furthermore, they lack a flexible andmobile 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-awareenactment of questionnaires on smart mobile deviceshas not been considered comprehensively by othergroups so far. In previous studies, we identifiedcrucial 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 alreadyshown several advantages in respect to data collec-tion and analysis, they have not been fully suitablefor realizing psychological questionnaires in the largescale 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 bycomputer 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-basedquestionnaires and supporting every phase with mo-bile technology has actually not been considered byother groups so far. However, there exists some workrelated 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) providesa web-based survey system, which provides a varietyof different input types for the questions used withina questionnaire. QuON does not use a model-basedrepresentation to specify a questionnaire as in our ap-proach. Another distinctive characteristic of QuON isthe webbased-only approach. Especially in psycho-logical field studies, the latter will result in problemsas the QuON platform does not use responsive web-design.

Movilitas (Movilitas, 2013) applies SAP SybaseUnwired Platform to enable mobile data collection forbusiness scenarios. The Sybase Unwired Platform isa 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 canbe used to realize mobile electronic questionnaires indomains 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 onthe same notation as used for business process mod-eling. In particular, a transformation method is intro-duced, which applies these patterns to automaticallyderive user interfaces by establishing a bidirectionalmapping between process model and user interface.

7 SUMMARY & OUTLOOK

In this paper, limitations of paper-based question-naires for data collection were discussed. To dealwith these limitations, we derived characteristic re-quirements for electronic questionnaire applications.In order to meet these requirements, we suggested theuse of process management technology. According tothe mental model introduced, a questionnaire and itslogic can be described in terms of a process model at ahigher level of abstraction. To evaluate our approach,a sophisticated application scenario from the psycho-logical domain was considered. We have shown howa questionnaire can be mapped to a process model.

In the interviews we conducted with domain ex-perts as well as from other implemented businessapplications we elaborated general requirements forflexible mobile data collection on smart mobile de-vices. These cover major aspects such as the secureand encrypted communication. Note that the latteris crucial, especially in the medical and psychologi-cal domains, which both deal with sensitive informa-

tion of the subjects involved. We further presentedan architecture enabling such mobile data collectionapplications based on a smart mobile device and aprocess engine. As another contribution, we demon-strated the feasibility of our proof-of-concept appli-cation. Several features as well as problems regard-ing the implementation and communication with theserver component, hosting the process engine, havebeen highlighted. Finally, we discussed the benefitsof using process-aware questionnaire application formobile data collection.

In future work, we plan to extend our approachwith 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 permanentinternet connection is available. We consider this as afundamental feature for enabling flexible data collec-tion applications on smart mobile devices. However,this will be accompanied with other problems, suchas 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 samelevel of information. In addition, we want to concep-tualize a generic questionnaire system, which is ableto support the complete lifecycle of a questionnaire.To disseminate this system among domain experts be-ing unfamiliar and unaware of modeling process logicwith standard notations, in addition, an easy to under-stand, but still precise notation for defining process-aware questionnaires is needed. To further enhancedata analysis capabilities (e.g., further analysis of thegiven answers), we have started to integrate sensorsmeasuring 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 anda significant decrease in workload. In particular, it en-ables a high level of abstraction in defining electronicquestionnaires that may run on smart mobile devices.

Acknowledgement

Supported by funds from the program Research initia-tives, intrastructure, network and transfer plattformsin the framework of the DFG Excellence Initiative -Third Funding Line.

REFERENCES

Anandarajan, M., Anandarajan, A., and Srinivasan,C. A. (2003). Business intelligence tech-

niques: a perspective from accounting and fi-nance. Springer.

Business Process Model (2011). Business ProcessModel and Notation (BPMN) Version 2.0. OMGSpecification, Object Management Group.

Crombach, A., Nandi, C., Bambonye, M., Liebrecht,M., Pryss, R., Reichert, M., Elbert, T., andWeierstall, R. (2013). Screening for mental dis-orders in post-conflict regions using computerapps - a feasibility study from burundi. InXIII Congress of European Society of TraumaticStress Studies (ESTSS) Conference.

Curbera, F., Duftler, M., Khalaf, R., Nagy, W., Mukhi,N., and Weerawarana, S. (2002). Unravelingthe Web services web: an introduction to SOAP,WSDL, and UDDI. IEEE, Internet Computing,6(2):86–93.

Dadam, P. and Reichert, M. (2009). The ADEPTProject: A Decade of Research and Develop-ment for Robust and Flexible Process Support -Challenges and Achievements. Computer Sci-ence - Research and Development, 23(2):81–97.

Electric Paper Evaluationssysteme (2013). EvaSys.http://www.evasys.de/. last visited: 05. Novem-ber 2013.

Isele, D., Ruf-Leuschner, M., Pryss, R., Schauer, M.,Reichert, M., Schobel, J., Schindler, A., and El-bert, T. (2013). Detecting adverse childhood ex-periences with a little help from tablet comput-ers. In XIII Congress of European Society ofTraumatic Stress Studies (ESTSS) Conference.

Kolb, J., Hubner, P., and Reichert, M. (2012). Au-tomatically Generating and Updating User In-terface Components in Process-Aware Informa-tion Systems. In 20th Int’l Conference on Co-operative Information Systems, number 7565 inLNCS, pages 444–454. Springer.

Kolb, J. and Reichert, M. (2013). A flexible approachfor abstracting and personalizing large businessprocess models. Applied Computing Review,13(1):6–17.

Kunze, C. P., Zaplata, S., and Lamersdorf, W. (2007).Mobile processes: Enhancing cooperation in dis-tributed mobile environments. Journal of Com-puters, 2(1):1–11.

Liebrecht, M. (2012). Technische Konzep-tion und Realisierung einer mobilen Anwen-dung fur den Konstanzer Index zur Erhebungvon psychosozialen Belastungen wahrend derSchwangerschaft. Diploma Thesis, University ofUlm.

Movilitas (2013). Movilitas Consulting AG.

http://www.movilitas.com/. last visited: 04.November 2013.

Paul, D., Wallis, M., Henskens, F., and Nolan, K.(2013). QuON: A Generic Platform for the Col-lation and Sharing of Web Survey Data. Interna-tional Conference on Web Information Systemsand Technologies.

Pryss, R., Langer, D., Reichert, M., and Hallerbach,A. (2012). Mobile Task Management for Medi-cal Ward Rounds - The MEDo Approach. In 1stInt’l Workshop on Adaptive Case Management(ACM’12), BPM’12 Workshops, number 132 inLNBIP, pages 43–54. Springer.

Pryss, R., Musiol, S., and Reichert, M. (2013). Col-laboration Support Through Mobile Processesand Entailment Constraints. In 9th IEEE Int’lConference on Collaborative Computing: Net-working, Applications and Worksharing. IEEEComputer Society Press.

Pryss, R., Tiedeken, J., Kreher, U., and Reichert,M. (2010a). Towards flexible process supporton mobile devices. In Proc CAiSE’10 Forum- Information Systems Evolution, number 72 inLNBIP, pages 150–165. Springer.

Pryss, R., Tiedeken, J., and Reichert, M. (2010b).Managing Processes on Mobile Devices: TheMARPLE Approach. In CAiSE’10 Demos.

Reichert, M. and Dadam, P. (1998). ADEPTflex-Supporting Dynamic Changes of WorkflowsWithout Losing Control. Journal of IntelligentInformation Systems, Special Issue on WorkflowManagement Systems, 10(2):93–129.

Reichert, M. and Dadam, P. (2009). Enabling Adap-tive Process-aware Information Systems withADEPT2. In Cardoso, J. and van der Aalst, W.,editors, Handbook of Research on Business Pro-cess Modeling, pages 173–203. Information Sci-ence Reference, Hershey, New York.

Reichert, M., Dadam, P., Rinderle-Ma, S., Jurisch,M., Kreher, U., and Goeser, K. (2009). Ar-chitecural Principles and Components of Adap-tive Process Management Technology. In PRIM-IUM - Process Innovation for Enterprise Soft-ware, number P-151 in Lecture Notes in Infor-matics (LNI), pages 81–97.

Reichert, M. and Weber, B. (2012). Enabling Flex-ibility in Process-Aware Information Systems:Challenges, Methods, Technologies. Springer,Berlin-Heidelberg.

Robecke, A., Pryss, R., and Reichert, M. (2011).DBIScholar: An iPhone Application for Per-forming Citation Analyses. In CAiSE Forum-

2011, volume Vol-73 of Proc CAiSE’11 Forum.CEUR Workshop Proceedings.

Ruf-Leuschner, M., Pryss, R., Liebrecht, M., Schobel,J., Spyridou, A., Reichert, M., and Schauer, M.(2013). Preventing further trauma: KINDEXmum screen - assessing and reacting towardspsychosocial risk factors in pregnant womenwith the help of smartphone technologies. InXIII Congress of European Society of TraumaticStress Studies (ESTSS) Conference.

Schindler, A. (2013). Technische Konzeption undRealisierung des MACE-Tests mittels mobilerTechnologie. Bachelor Thesis, University ofUlm.

Schobel, J., Schickler, M., Pryss, R., Nienhaus, H.,and Reichert, M. (2013). Using Vital Sensors inMobile Healthcare Business Applications: Chal-lenges, Examples, Lessons Learned. In 9th Int’lConference on Web Information Systems andTechnologies (WEBIST 2013), Special Sessionon Business Apps, pages 509–518.

SurveyMonkey (2013). SurveyMonkey: Free on-line survey software & questionnaire tool.http://www.surveymonkey.com/. last visited: 14.May 2013.

van der Aalst, W. M., Reijers, H. A., Weijters, A. J.,van Dongen, B. F., Alves de Medeiros, A., Song,M., and Verbeek, H. (2007). Business processmining: An industrial application. InformationSystems, 32(5):713–732.

Wakholi, P., Chen, W., and Klungsøyr, J. (2011).Workflow support for mobile data collection.In Enterprise, Business-Process and InformationSystems Modeling, pages 299–313. Springer.

Weber, B., Reichert, M., Mendling, J., and Reijers, H.(2011). Refactoring large process model reposi-tories. Computers in Industry, 62(5):467–486.