By Ed H. Chi (auth.)
Fundamental recommendations in figuring out details were elusive for a very long time. the sphere of man-made Intelligence has proposed the Turing attempt with the intention to try out for the "smart" behaviors of laptop courses that express human-like characteristics. such as the Turing try out for the sector of Human details interplay (HII), getting details to the folk that want them and aiding them to appreciate the data is the recent problem of the internet period. In a brief period of time, the infrastructure of the net grew to become ubiquitious not only by way of protocols and transcontinental cables but additionally by way of daily units able to recalling network-stored information, occasionally twine lessly. for this reason, as those infrastructures turn into fact, our cognizance on HII concerns must shift from details entry to details sensemaking, a comparatively new time period coined to explain the method of digesting details and figuring out its constitution and intricacies that allows you to make judgements and take action.
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Extra info for A Framework for Visualizing Information
Cone Trees , Hyperbolic Trees , TreeMaps  and Disk Trees  can be used to visualize a wide variety of hierarchical data. • Visualization Transformation Operators can be applied to data domains with similar goals. For example, multi-dimensional dustering can be used to reduce the dimensionality of any problern that can be formulated using a feature space. Breadth-first traversal can be used to produce a hierarchy out of a graph network. • Data Transformation operators are specific to the particular data structure from an application domain, because they take the data structure as input, and output an analytical abstraction.
Moreover, the added simplification of the operator space in the data-centric approach does not represent significant complexity savings. Since a visualization system needs to deal with many different data types, the operator space of such systems will be very complex anyway. Our operator-centric approach is a significant departure from past visualization models, and gives the Data State Model the ability to classify different operators in finer grain detail. By taking the approach of classifying the space of operators in finer detail, we can more easily reuse operator definitions, and actual code implementations.
In our experience, this kind of predictability is crucial in the success of the Visualization Spreadsheet. Second, application programmers can use this model to extend general visualization system to new data domains. This framework enables programmers to identify operators that are not domain-specific and, hence, that they can easily reuse. For example, in the Visualization Spreadsheet, the system must be extended with domain-specific analysis operators such as textual document dustering and visualization to enable it tobe applicable in specific application tasks, such as analyzing financial documents.