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Trajectory Data Analysis Over a Cloud-Based Framework for Smart City Analytics

  • Eugenio Cesario
  • Carmela Comito
  • Domenico Talia
Part of the Internet of Things book series (ITTCC)

Abstract

The chapter presents a Cloud-based framework that can be tailored to be used in different scenarios of urban planning and management occuring in Smart Cities. The focus is on the management of large-scale socio-geographic data obtained through the trajectories traced by smart objects. Our goal is to mine human activities and routines from this socio-geographic data in order to catch user’s behaviour. To this aim, we introduce a methodology for trajectory pattern mining consisting in (a) finding frequent regions, more densely passed through ones, and (b) extracting trajectory patterns from those regions. Experimental evaluation shows that due to complexity and large data involved in the application scenario, the trajectory pattern mining process can take advantage from a parallel execution environment offered by a Cloud architecture.

Keywords

Cloud Computing Smart City Cloud Infrastructure Trajectory Data Smart Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research work has been partially funded by the MIUR projects TETRIS (PON01_00451) and DICET-INMOTO (PON04a2_D).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eugenio Cesario
    • 1
  • Carmela Comito
    • 1
  • Domenico Talia
    • 2
  1. 1.ICAR-CNRRende (CS)Italy
  2. 2.ICAR-CNR and DIMES-UNICALRende (CS)Italy

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