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Ouvrages Année : 2018

Special issue on extracting crowd intelligence from pervasive and social big data

Résumé

With the prevalence of ubiquitous computing devices (smartphones, wearable devices, etc.) and social network services (Facebook, Twitter, etc.), humans are generating massive digital traces continuously in their daily life. Considering the invaluable crowd intelligence residing in these pervasive and social big data, a spectrum of opportunities is emerging to enable promising smart applications for easing individual life, increasing company profit, as well as facilitating city development. However, the nature of big data also poses fundamental challenges on the techniques and applications relying on the pervasive and social big data from multiple perspectives such as algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, data heterogeneity and system scalability. This special issue presents the state-of-the-art research achievements in addressing these challenges

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Dates et versions

hal-01823619 , version 1 (26-06-2018)

Identifiants

  • HAL Id : hal-01823619 , version 1

Citer

Leye Wang, Vincent Gauthier, Guanling Chen, Luis  moreira‑matias (Dir.). Special issue on extracting crowd intelligence from pervasive and social big data. Springer, 9, n°2, pp.411, 2018. ⟨hal-01823619⟩
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