Synergistic User \(\longleftrightarrow\) Context Analytics

  • Andreea Hossmann-Picu
  • Zan Li
  • Zhongliang Zhao
  • Torsten Braun
  • Constantinos Marios Angelopoulos
  • Orestis Evangelatos
  • José Rolim
  • Michela Papandrea
  • Kamini Garg
  • Silvia Giordano
  • Aristide C. Y. Tossou
  • Christos Dimitrakakis
  • Aikaterini Mitrokotsa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 399)

Abstract

Various flavours of a new research field on (socio − )physicalorpersonalanalytics 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.

Keywords

crowd-sensing information fusion crowd-sensing analytics 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andreea Hossmann-Picu
    • 1
  • Zan Li
    • 1
  • Zhongliang Zhao
    • 1
  • Torsten Braun
    • 1
  • Constantinos Marios Angelopoulos
    • 2
  • Orestis Evangelatos
    • 2
  • José Rolim
    • 2
  • Michela Papandrea
    • 3
  • Kamini Garg
    • 3
  • Silvia Giordano
    • 3
  • Aristide C. Y. Tossou
    • 4
  • Christos Dimitrakakis
    • 4
  • Aikaterini Mitrokotsa
    • 4
  1. 1.University of BernBernSwitzerland
  2. 2.University of GenevaGenevaSwitzerland
  3. 3.SUPSIMannoSwitzerland
  4. 4.Chalmers UniversityGöteborgSweden

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