Synergistic User $\longleftrightarrow$ Context Analytics
Theus Hossmann, Zan Li, Zhongliang Zhao, Torsten Ingo Braun, Constantinos Marios Angelopoulos, Orestis Evangelatos, Andrea Roli, Michela Papandrea, Kamini Garg, Silvia Giordano, Aristide C. Y. Tossou, Christos Dimitrakakis, Aikaterini Mitrokotsa · Advances in intelligent systems and computing · 2015
Various flavours of a new research field on (socio − )physical or personal analytics have emerged, with the goal of deriving semanticallyrich insights from people’s low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental and application-specific data, to better capture the interactions between users and their context, in addition to those among users. We provide some example use cases and present our ongoing work towards a synergistic analytics platform: a testbed based on mobile crowdsensing and IoT, a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights.