The article tries to interpret the visualization of complex data using a task by data type taxonomy that makes user exploration of the visualization fast and easy. The present day visualizations are predominantly in one or the other forms of data types stated in the paper. The author's view on creating visualizations that help in exploring only the points of interest with growing volume is what is expected from the visualization as a lot of data need to be represented in the visualization, but still only some part of it may be of interest to a particular group of users while another group of users may be interested in another part of the visualization.
The perception of a visualization is complete in most cases after the information seeking mantra as most of the user expected information is analyzed after those steps. But, a lot of other tasks like relation, history and on demand details are required when one or more data types are present in the visualization and data needs to be extracted from them. The author's view of taxonomy and tasks is very efficient in analyzing complex graphical visualizations rapidly and extracting required information.
The perception of a visualization is complete in most cases after the information seeking mantra as most of the user expected information is analyzed after those steps. But, a lot of other tasks like relation, history and on demand details are required when one or more data types are present in the visualization and data needs to be extracted from them. The author's view of taxonomy and tasks is very efficient in analyzing complex graphical visualizations rapidly and extracting required information.
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