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In this article, the authors shed light on the challenge of underutilizing the big data generated by smart cities from a machine learning perspective. In particular, they discuss the phenomenon of wasting unlabeled data and they argue that semi-supervision is a must for smart cities to address this challenge. Finally, they propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities.In this article, the authors shed light on the challenge of underutilizing the big data generated by smart cities from a machine learning perspective. In particular, they discuss the phenomenon of wasting unlabeled data and they argue that semi-supervision is a must for smart cities to address this challenge. Finally, they propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities.
The current paper by Moreno et al. (2016) attempts to analyze the interest of big data for smart cities through the presentation of some applications in two scenarios. The first scenario deals with large volumes of heterogeneous information for use in smart building applications and the second one is centered on the public tram service in the city of Murcia, Spain.