A Configurator Component for End-User Defined Mobile Data Collection Processes

The widespread dissemination of smart mobile devices offers promising perspectives for collecting huge amounts of data. When realizing mobile data collection applications (e.g., to support clinical trials), challenging issues arise. For example, many real-world projects require support for heterogeneous mobile operating systems. Usually, existing data collection approaches are based on specifically tailored mobile applications. As a drawback, changes of a data collection procedure require costly code adaptations. To remedy this drawback, we implemented a model-driven approach that enables end-users to realize mobile data collection applications themselves. This paper demonstrates the developed configurator component, which enables domain experts to implement digital questionnaires. Altogether, the configurator component allows for the fast development of questionnaires and hence for collecting data in large-scale scenarios using smart mobile devices

A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

Development of Mobile Data Collection Applications by Domain Experts: Experimental Results from a Usability Study

Despite their drawbacks, paper-based questionnaires are still used to collect data in many application domains. In the QuestionSys project, we develop an advanced framework that enables domain experts to transform paper-based instruments to mobile data collection applications, which then run on smart mobile devices. The framework empowers domain experts to develop robust mobile data collection applications on their own without the need to involve programmers. To realize this vision, a configurator component applying a model-driven approach is developed. As this component shall relieve domain experts from technical issues, it has to be proven that domain experts are actually able to use the configurator properly. The experiment presented in this paper investigates the mental efforts for creating such data collection applications by comparing novices and experts. Results reveal that even novices are able to model instruments with an acceptable number of errors. Altogether, the QuestionSys framework empowers domain experts to develop sophisticated mobile data collection applications by orders of magnitude faster compared to current mobile application development practices

Measuring Mental Effort for Creating Mobile Data Collection Applications

To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N=80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials

Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Background: Many research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios. Objective: In order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants. Methods: Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework. Results: Statistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations). Conclusions: In conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own

End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains

Developing an Extendable Process Engine using Cross-Platform Technologies

Despite the increasing digitization in everyday work and industry, data collection is still often based on paper-based questionnaires. One of the areas of application where the disadvantages come to bear are large-scale studies, such as clinical trials. In such studies, an enormous amount of paper and staff is needed for transcription, which leads to logistical problems as well as error susceptibility. The reasons why paper-based questionnaires are still used are often a lack of IT knowledge of the involved, difficult to use existing software, as well as high costs for the development of new customized software. The QuestionSys framework aims to solve these problems. It supports all steps of data collection from the creation of a questionnaire, through its execution on mobile devices, to the analysis of the collected data. In order to ensure a high degree of flexibility when creating questionnaires, questionnaires are mapped to process models which can then be executed by mobile devices. In the context of this thesis, a lightweight mobile process engine has been developed that allows to execute the process models of the QuestionSys framework. The focus was on process execution, support for several operating systems and easy extensibility. For this purpose, this thesis discusses related work, before the architecture of the engine is presented on the basis of defined requirements. In the following chapter, parts of the implementation are explained, which ultimately leads to an outlook

Technical Conception and Implementation of a Configurator Environment for Process-aware Questionnaires Based on the Eclipse Rich Client Platform

Questionnaires are one of the fastest and easiest methods for inquiring information about a required topic. Especially the more and more advancing online connectivity and mobile accessibility offer additional possibilities, like working collaboratively from different places or store results centrally, to make it an even faster and more comfortable tool for data collection. Several existing software approaches to create questionnaires - called questionnaire configurators - are available and expensively tailor functionality to the needs of the target group. In this thesis an approach is presented, which outsources tasks to functionality provided by a process-aware information system (PAIS). To offer extensibility for upcoming needs, a generic questionnaire model is the basis for an integration of a PAIS into a questionnaire configurator environment. The result is called a process-aware questionnaire configurator and is discussed regarding its architecture and implementation. With an implemented prototype of a process-aware questionnaire configurator an insight is granted into a concrete implementation based on the Eclipse Rich Client Platform

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems