Synergistic user ↔ context analytics
Book chapter, 2016

© Springer International Publishing Switzerland 2016. 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.


Crowd-sensing analytics

Information fusion


A. Hossmann-Picu

University of Bern

Z. Li

University of Bern

Z. Zhao

University of Bern

T. Braun

University of Bern

C.M. Angelopoulos

University of Geneva

O. Evangelatos

University of Geneva

J. Rolim

University of Geneva

M. Papandrea

K. Garg

S. Giordano

Aristide Tossou

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Aikaterini Mitrokotsa

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Advances in Intelligent Systems and Computing

2194-5357 (ISSN)


Subject Categories

Computer and Information Science





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