Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Why data needs more attention in architecture design - experiences from prototyping a large-scale mobile app ecosystem

: Naab, Matthias; Braun, Susanne; Lenhart, Torsten; Hess, Steffen; Eitel, Andreas; Magin, Dominik Pascal; Carbon, Ralf; Kiefer, Felix


Bass, Len (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
12th Working IEEE/IFIP Conference on Software Architecture, WICSA 2015. Proceedings : 4-8 May 2015, Montreal, Quebec, Canada
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2015
ISBN: 978-1-4799-1922-2
Working Conference on Software Architecture (WICSA) <12, 2015, Montréal>
Fraunhofer IESE ()
mobile computing; software architecture; software quality; architecture design; Data Modeling; app ecosystem; architecture design; computer architecture; context; mobile communication systems

Data is of great importance in computer science and in particular in information systems and how data is treated has major impact on a system's quality attributes. Nevertheless, software architecture research, literature, and practice often neglect data and focus instead on other architectural topics like components and connectors or the management of architecture decisions in general. This paper contributes experiences from the prototyping of a large-scale mobile app ecosystem for the agricultural domain. Architectural drivers like multi-tenancy, different technical platforms and offline capability led to deep reasoning about data. In this paper, we describe the architectural decisions made around data in the app ecosystem and we present our lessons learned on technical aspects regarding data, but also on data modeling and general methodical aspects how to treat data in architecting. We want to share these experiences with the research community to stimulate more research on data in software architecture and we want to give practitioners usable hints for their daily work around data in constructing large information systems and ecosystems.