A Framework for Visualizing Information by Ed H. Chi (auth.)

By Ed H. Chi (auth.)

Fundamental ideas in realizing details were elusive for a very long time. the sector of synthetic Intelligence has proposed the Turing try for you to attempt for the "smart" behaviors of desktop courses that express human-like characteristics. comparable to the Turing try out for the sphere of Human­ details interplay (HII), getting details to the folks that want them and assisting them to appreciate the knowledge is the hot problem of the internet period. In a quick period of time, the infrastructure of the internet 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 facts, occasionally twine­ lessly. accordingly, as those infrastructures turn into truth, our realization on HII concerns must shift from info entry to details sensemaking, a comparatively new time period coined to explain the method of digesting details and figuring out its constitution and intricacies so one can make judgements and take action.

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Classification of Operators using the Framework Using the above model, we can classify a variety of operators according to what stage of the pipeline the operator is involved in. The classification of visualization operators shows that each 2 the number of times a particular link to its children is traveled 30 A FRAMEWORK FOR VISUALIZING INFORMATION operator either performs a transformation that takes information between stages, or generates another information set within the same stage. That is, some operators work within a single stage, while other operators work across different stages.

The framework facilitates a new way of exploring the space of visualization operators. Using the Data State Model, this method forms the basis of an evaluation technique for operators in visualizations. By applying this operator analysis to various visualizations, we can point researchers toward areas where particular operators are missing from a given system or technique. We can also use this model to compare different interaction models in visualizations. This will enable other researchers to characterize various interaction techniques, and capture design requirements for new application domains, and develop new and novel operators.

Item Attribute: Each item stores its associated access frequency and hop counts2 • Each item also contains information on its life cycle (newly created, deleted, moved, or modified). Item Vector: Each item also has a set of vectors that specify the precomputed Spreading Activation patterns. Moreover, the Spreading Activation is computed over the evolving document collection, which enables the comparison of results from different time periods. 4. Classification of Operators using the Framework Using the above model, we can classify a variety of operators according to what stage of the pipeline the operator is involved in.

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