Synergistic user ↔ context analytics

This data was imported from Scopus:

Authors: Hossmann-Picu, A. et al.

http://eprints.bournemouth.ac.uk/24078/

Journal: Advances in Intelligent Systems and Computing

Volume: 399

Pages: 163-172

ISBN: 9783319257310

ISSN: 2194-5357

DOI: 10.1007/978-3-319-25733-4_17

© 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.

This data was imported from Web of Science (Lite):

Authors: Hossmann-Picu, A. et al.

http://eprints.bournemouth.ac.uk/24078/

Journal: ICT INNOVATIONS 2015: EMERGING TECHNOLOGIES FOR BETTER LIVING

Volume: 399

Pages: 163-172

eISSN: 2194-5365

ISBN: 978-3-319-25731-0

ISSN: 2194-5357

DOI: 10.1007/978-3-319-25733-4_17

The data on this page was last updated at 05:09 on February 27, 2020.