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Smart cities, urban sensing, and big data: mining geo-location in social networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sacco, Daniele Motta, Gianmario You, Linlin Bertolazzo, N. Carini, Franca Ma, Tianyi |
| Copyright Year | 2017 |
| Abstract | Location-based social networks offer spatiotemporal information which can be accessed through public application programming interfaces and have drawn the interest of researchers with diverse scientific backgrounds. This availability of data enables the potential use of geo-located content as an additional, low-cost and infrastructureless source of information for urban sensing in smart cities. All these aspects along with the need for real-time analytics for urban sensing, take us to Big Data management and its related issues. Real-time urban sensing uses citizens as active and passive sensors and can reveal important insights about human behavior in the city. A systematic literature review outlines related works and gaps in current research. In this chapter, we propose a reference model to exploit Big Data for urban sensing and we validate it using a case study. Finally, we give recommendations for future research about location and mobility mining of social network data. |
| Starting Page | 59 |
| Ending Page | 84 |
| Page Count | 26 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/B978-0-12-812013-2.00005-8 |
| Alternate Webpage(s) | http://camellia.unipv.it/servizi/images/publication/2013/2013-88.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
