Synergistic user ↔ context analytics
Kapitel i bok, 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

Universität Bern

Z. Li

Universität Bern

Z. Zhao

Universität Bern

T. Braun

Universität Bern

C.M. Angelopoulos

Universite de Geneve

O. Evangelatos

Universite de Geneve

J. Rolim

Universite de Geneve

M. Papandrea

K. Garg

S. Giordano

Aristide Tossou

Chalmers, Data- och informationsteknik, Datavetenskap

Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Datavetenskap

Aikaterini Mitrokotsa

Chalmers, Data- och informationsteknik, Nätverk och system

Advances in Intelligent Systems and Computing

2194-5357 (ISSN)



Data- och informationsvetenskap





Mer information

Senast uppdaterat