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Synergistic user ↔ context analytics

A. Hossmann-Picu ; Z. Li ; Z. Zhao ; T. Braun ; C.M. Angelopoulos ; O. Evangelatos ; J. Rolim ; M. Papandrea ; K. Garg ; S. Giordano ; Aristide C. Y. Tossou (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)) ; Christos Dimitrakakis (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)) ; Aikaterini Mitrokotsa (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) )
Advances in Intelligent Systems and Computing (2016)
[Kapitel]

© 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.

Nyckelord: Crowd-sensing , Crowd-sensing analytics , Information fusion



Denna post skapades 2016-05-12. Senast ändrad 2016-08-12.
CPL Pubid: 236407

 

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