author = "Wong, Christian and Oliveira, Maria Cristina F. and Minghim, 
          affiliation = "{Instituto de Ciencias Matem{\^a}ticas e de 
                         Computa{\c{c}}{\~a}o} and {Instituto de Ciencias 
                         Matem{\^a}ticas e de Computa{\c{c}}{\~a}o} and {Instituto de 
                         Ciencias Matem{\^a}ticas e de Computa{\c{c}}{\~a}o}",
                title = "Multidimensional projections to explore time-varying multivariate 
                         volume data",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Visualization, scientific visualization, multidimensional 
                         projections, exploratory volume visualization.",
             abstract = "Multidimensional Projections (MPs) have become popular as visual 
                         data analysis tools in several application domains, including 
                         Scientific Visualization. Current techniques are fast, precise and 
                         capable of handling local and global data features, having 
                         successfully supported spatial and abstract data visualizations. 
                         However, two major shortcomings hinder their application for 
                         exploratory analysis of time-varying multivariate volumetric data. 
                         Current techniques lack visual coherence when applied to data 
                         collected across consecutive time stamps and offer little support 
                         to investigating attribute-specific questions. Both are relevant 
                         properties when analysing time varying volumes. In this paper we 
                         revisit projection methods from this perspective and introduce 
                         modifications into two existing high-performance techniques to 
                         ensure temporal coherence. We also propose a hybrid visualization 
                         strategy that can assist users investigating the role of a 
                         specific attribute on data behavior through time. We illustrate 
                         how our approaches enhance projection-based visual exploration of 
                         time-varying multivariate volume data with their application to 
                         data sets from three distinct simulations, made available for 
                         editions of the IEEE Visualization Contest.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "paper114545_camera-ready.pdf",
        urlaccessdate = "2020, Dec. 03"