@InProceedings{JrSantMace:2017:ViAnPr,
               author = "Junior, Antonio Jose Melo Leite and Santos, Emanuele and vidal, 
                         Creto Augusto and Macedo, Jose Antonio Fernandes de",
          affiliation = "Virtual University Institute - Federal University of Ceara - 
                         Fortaleza, Brazil and Department of Computing - Federal University 
                         of Ceara - Fortaleza, Brazil and Department of Computing - Federal 
                         University of Ceara - Fortaleza, Brazil and Department of 
                         Computing - Federal University of Ceara - Fortaleza, Brazil",
                title = "Visual Analysis of Predictive Suffix Trees for Discovering 
                         Movement Patterns and Behaviors",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Visual Analysis, Movement Pattern, Predictive Suffix Trees.",
             abstract = "The use of GPS-equipped devices has allowed generating and storing 
                         data related to massive amounts of moving objects, promoting many 
                         solutions to movement prediction problems. Movement prediction 
                         became essential to perform tasks in several areas ranging from 
                         analysis of the popularity of geographic regions; and management 
                         of traffic and transportation; to recommendations in 
                         location-based social networks. To explore this type of data is a 
                         complex task because one must deal simultaneously with space, time 
                         and probability. In this work, we apply the branching time concept 
                         to visual analytics, proposing an approach that supports movement 
                         prediction using Probabilistic Suffix Trees. We try to substitute 
                         the traditional evaluation method, based on reading texts, by an 
                         interactive visual solution. To validate the proposed solution, we 
                         developed and tested a visualization tool using a real dataset. It 
                         assisted experts to quickly identify where a person lives, where 
                         she works and to recognize some of her movement patterns and 
                         probable behaviors.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.20",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.20",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFRDSH",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRDSH",
           targetfile = "PID4960307.pdf",
        urlaccessdate = "2024, Apr. 29"
}