Mob-Warehouse: a semantic approach for mobility analysis with a Trajectory Data Warehouse (Contributo in atti di convegno)

Type
Label
  • Mob-Warehouse: a semantic approach for mobility analysis with a Trajectory Data Warehouse (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Alternative label
  • Wagner R., de Macedo J. A., Raffaeta' A., Renso C., Roncato A., Trasarti R. (2013)
    Mob-Warehouse: a semantic approach for mobility analysis with a Trajectory Data Warehouse
    in SEEK - 7th International Workshop on Semantic and Conceptual Issues in GIS, Hong Kong, China, 12 November 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Wagner R., de Macedo J. A., Raffaeta' A., Renso C., Roncato A., Trasarti R. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto: SEEK - Semantic EnrichmEnt of trajectory Knowledge discovery Grant agreement: 295179 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://cs.ulb.ac.be/conferences/secogis2013/ (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Universidade Federal do Cear?a, Brazil; Universidade Federal do Cear?a, Brazil; Ca' Foscari University of Venice, Italy; CNR-ISTI, Pisa, Italy; Ca' Foscari University of Venice, Italy; CNR-ISTI, Pisa, Italy. (literal)
Titolo
  • Mob-Warehouse: a semantic approach for mobility analysis with a Trajectory Data Warehouse (literal)
Abstract
  • The effective analysis and understanding of huge amount of mobility data have been a hot research topic in the last few years. Some proposals addressed the definition of Trajectory Data Warehouses (TDW) as a way to represent and aggregate mobility data, where the ba- sic object is the trajectory. In this paper, we introduce Mob-Warehouse, a TDW which goes a step further since it models trajectories enriched with semantics. In Mob-Warehouse, the unit of movement is the (spatio- temporal) point enriched with several non spatio-temporal dimensions including the activity, the transportation means and the mobility pat- tern. This model allows us to answer the classical Why, Who, When, Where, What, How questions providing an aggregated view of different aspects of the user movements, no longer limited to space and time. We briefly present an experiment of Mob-Warehouse on a real dataset. (literal)
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