Location based social networks offer spatio-temporal information which can be accessed through public Application Programming Interfaces (APIs) and drew the interest of researchers with diverse scientific backgrounds. This availability of data enables a potential use of geo-located content as an additional, low cost and infrastructure-less source of information for urban sensing in Smart Cities. All these aspects bounded with the need of real-time analytics for urban sensing takes to Big Data management and its related issues. A systematic literature review outlines related works and gaps in current research. We propose a reference model to exploit Big Data and Open Data for urban sensing and we validate it by a case study. Finally, we give recommendations for future research about location and mobility mining of social network data.
Smart Cities, Urban Sensing and Big Data: Mining Geo-location in Social Networks
MOTTA, GIANMARIO PIERO ANTONIO;
In corso di stampa
Abstract
Location based social networks offer spatio-temporal information which can be accessed through public Application Programming Interfaces (APIs) and drew the interest of researchers with diverse scientific backgrounds. This availability of data enables a potential use of geo-located content as an additional, low cost and infrastructure-less source of information for urban sensing in Smart Cities. All these aspects bounded with the need of real-time analytics for urban sensing takes to Big Data management and its related issues. A systematic literature review outlines related works and gaps in current research. We propose a reference model to exploit Big Data and Open Data for urban sensing and we validate it by a case study. Finally, we give recommendations for future research about location and mobility mining of social network data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
