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

Universität Ulm

Fakultätsöffentliche Vorstellung des Promotionsvorhabens (Grüner Vortrag), Johannes Schobel, Ort: O28 / 1002, Datum: 06.04.2017, Zeit: 13:00 Uhr

In the light of digital transformation and cloud computing, mobile technology has become a salient factor for large-scale data collection scenarios. In this context, structured instruments (e.g., questionnaires) are commonly used to collect data in various application domains, like healthcare, psychology, or social sciences. However, the latter are distributed and filled out in a traditional paper-and-pencil fashion. The widespread use of smart mobile devices offers promising perspectives with respect to the controlled collection of high-quality and accurate data. The design, implementation and deployment of corresponding mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems (e.g., Android and iOS) need to be supported, taking the short release cycles of vendors into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, common usability guidelines need to be obeyed. Altogether, this turns both the programming and the maintenance of mobile data collection applications into a costly, time-consuming, and error-prone endeavor.

The talk will give insights into an advanced framework that allows transforming sophisticated paper-based instruments to mobile data collection applications. The latter, in turn, can then be run on heterogeneous smart mobile devices. In particular, the framework empowers domain experts (i.e., end users) to flexibly develop robust mobile data collection applications on their own without need to involve mobile application programmers. The framework will allow developing sophisticated mobile data collection applications by orders of magnitude faster compared to current practices on one hand. On the other, domain experts will be relieved from manual tasks, like digitizing the data collected.