@InProceedings{FerreiraOlivMontAlme:2015:TeGISp,
               author = "Ferreira, Karine Reis and Oliveira, Andre Gomes de and Monteiro, 
                         Antonio Miguel Vieira and Almeida, Diego Benincasa F. C. de",
          affiliation = "{nstituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Funda{\c{c}}{\~a}o de Ci{\^e}ncia, Aplica{\c{c}}{\~o}es e 
                         Tecnologia Espaciais (FUNCATE) and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Temporal GIS and spatiotemporal data sources",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Fileto, Renato and Korting, Thales Sehn",
                pages = "1--13",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 16. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The recent technological advances in geospatial data collection 
                         have created massive data sets with better spatial and temporal 
                         resolution than ever. To properly deal with these data sets, 
                         geographical information systems (GIS) must evolve to represent, 
                         access, analyze and visualize big spatiotemporal data in an 
                         efficient and integrated way. In this paper, we highlight 
                         challenges in temporal GIS development and present a proposal to 
                         overcome one of them: how to access spatiotemporal data sets from 
                         distinct kinds of data sources. Our approach uses Semantic Web 
                         techniques and is based on a data model that takes observations as 
                         basic units to represent spatiotemporal information from different 
                         application domains. We define a RDF vocabulary for describing 
                         data sources that store or provide spatiotemporal observations.",
  conference-location = "Campos do Jord{\~a}o",
      conference-year = "27 nov. a 02 dez. 2015",
                 issn = "2179-4820",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/3KP2RBP",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3KP2RBP",
           targetfile = "proceedings2015_p1.pdf",
        urlaccessdate = "27 abr. 2024"
}