This paper presents an interesting fundamental approach in seeking information from advance graphical interfaces and visualizations.
According to the author, information visualizations can be categorized into seven data types. He also defines seven tasks that can be performed on the visualization to get the most information out of them.
The author insists that to analyze any information visualization we should first get an overview of the entire set of data. Once we have the overview, we should filter out the data which we are not interested in. And the last step is to get the details on demand. I think we can relate this approach to plain data as well. For example, if someone is searching statistical data about city he would filter out the locality, region etc first.
I particularly liked the explanation where author corelates the dynamic queries with the direct-manipulation queries that are used for advance filtering. With regard to graphic visualizations, this is quite critical as with the emergence of Web 3.0 (HTML5 and CSS3), it may be difficult to quickly fetch structured data and display it fast enough on the user interfaces. As an explanation, it's surely better to invert the dataset while using NOT operator rather than fetching the entire set of data from the database.
Overall, I think this is an interesting paper that deals with future prospects of efficiently analyzing ever increasing information using graphical visualizations.
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