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The specification for the visual mappingĬontemplates both the storage of the data set to be represented, and the visual Serve as the basis to define the interactions that take place in each state and The application field and the visualization area. Representation presented includes the definitions proposed by the most relevantĪuthors in the area, achieving in this way an integration that lays theįoundation for the definition of a visualization process independent of We introduce a classification and a representation schema for the data, theĭata sets and the attributes involved in the visualization process. The main contributions of this thesis are:Ĭlassification for data, attributes and data sets in the visualization context. This representation must be sufficiently flexible to support the different classifications of data, attributes, data sets and visual mappings present in the visualization literature. In this context, it is necessary to define a representation for the data sets involved in the process.
The defined interactions will be applied to the states and transformations of the visualization process. The overall goal of this thesis is to define the interactions and a classification of interactions in visualization valid in different application domains. In this context, the definition of a taxonomy of the interactions in the visualization area is extremely necessary to achieve a better understanding of the interactions' design space. The properties of the visualization process determine that the user should be able to interact with the data and its intermediate representations, control the transformations and manipulate the visualizations. The UVM is a model of states and transformations that represents the flow of the data throughout the visualization process. In this context, the Unified Visualization Model (UVM) is defined. These common denominators suggest the need for a visualization model that is consistent for all visualization areas and valid for different application domains. Usually, different application domains require different visual representations, however, several of them share intermediate states of data, transformations, and/or require similar manipulations. The contribution of visualization to the exploration and understanding of these large data sets is highly significant. There are many ways to remove and .Nowadays, the vertiginous growth of information generates volumes of data that are increasingly larger and difficult to understand and analyze.
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