By Ed H. Chi (auth.)
Fundamental strategies in realizing info were elusive for a very long time. the sphere of man-made Intelligence has proposed the Turing try so as to try for the "smart" behaviors of laptop courses that express human-like traits. corresponding to the Turing try out for the sphere of Human info interplay (HII), getting details to the folks that want them and supporting them to appreciate the data is the hot problem of the internet period. In a brief period of time, the infrastructure of the internet grew to become ubiquitious not only when it comes to protocols and transcontinental cables but additionally by way of daily units in a position to recalling network-stored facts, occasionally cord lessly. for this reason, as those infrastructures develop into fact, our awareness on HII matters must shift from details entry to details sensemaking, a comparatively new time period coined to explain the method of digesting info and figuring out its constitution and intricacies as a way to make judgements and take action.
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Extra resources for A Framework for Visualizing Information
For example, in textual analysis, there are several different kinds of textual analysis algorithms, such as clustering, multi-dimensional scaling, principal component analysis, collaborative filtering, etc. Each of these algorithms may require different analytical abstractions, such as textual vectors, similarity scoring, or user ratings. 1 Example: Web Analysis Visualization Operators Here we will present a more extensive classification within a single complex data domain. In the next section, we will present a 32 A FRAMEWORK FOR VISUALIZING INFORMATION visualization spreadsheet example that analyzes Web usage data.
On the other extreme, we have full value operators that can only be interpreted as value operators, such as expanding an existing data set by adding a new data set. Operators that are not full view or full value operators lie in between the two extremes. One exarnple is the multi-dimensional scaling information processing technique, which reduces the dimensionality of data sets. Other examples of these types of operators include operators reIated to textual word frequency vectors, which are produced from a set of docurnents.
Fundamentally, each of these data domains have data and its associated visualized view, therefore filtering actions in these data domains will also have the same exact ambiguous meanings. So this problern exists even after careful consideration of the application task domain. The HorneFinder example emphasizes the difference between view and value. The value of a visualization is the raw data set being visualized. The view controls the way that this raw data is represented on the screen. In information visualization, since the data is represented abstractly on the screen, there is a distinct separation between the value of the data and the view of the data, and it is especially useful to represent the same data in many different ways.