Synergistic user ↔ context analytics
Authors: Hossmann-Picu, A., Angelopoulos, C.M. et al.
Journal: Advances in Intelligent Systems and Computing
Volume: 399
Pages: 163-172
ISSN: 2194-5357
DOI: 10.1007/978-3-319-25733-4_17
Abstract: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.
https://eprints.bournemouth.ac.uk/24078/
Source: Scopus
Synergistic User ⇆ Context Analytics
Authors: Hossmann-Picu, A., Angelopoulos, C.M. et al.
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
https://eprints.bournemouth.ac.uk/24078/
Source: Web of Science (Lite)
Synergistic user ↔ context analytics
Authors: Hossmann-Picu, A., Angelopoulos, C.M. et al.
Conference: ICT Innovations 2015: Emerging Technologies for Better Living
Pages: 163-172
Publisher: Springer
ISBN: 9783319257310
ISSN: 2194-5357
Abstract: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.
https://eprints.bournemouth.ac.uk/24078/
Source: BURO EPrints