The science of Interaction interactive elements and the way to query the knowledge building process

The Science of Interaction

Summary

Perceptual interactions and queries that grow in visual analytics cannot be freed. By interactively manipulating the visual interface---analysis----build, test, refine, and share knowledge. This article reflects the interactive challenges in visual research and development conferences. Identifying recent examples of visual analysis studies, with real progress in real-world interaction science goals, must include both theoretical and testable mechanisms for interacting with people and information. Seven areas of the next 5 years of visual analysis: pervasive, representational interaction, access to user intentionality, knowledge-based interface, and interaction assessment. Ultimately, the scientific interaction goal is to support visual analysis, human and machine interaction groups through the perception and implementation of the best practices in the visual display of characterization and manipulation.

Interactive elements, the field of information visualization divides the interaction into two levels, low levels of interaction (interaction between user and software interface), high level of interaction (user interaction with the information space).

Three ways of acquiring knowledge,

1 abduction-induced, observational reports obtained from exploratory analysis, stimulated by an "instinctive request" may be assumed. (What seems to make sense to users?) The process of becoming familiar with new data spaces is considered to be the induction process: Users begin to understand the problem and form goals, as well as confirm data resources if the data is not given in advance. Familiar process design to test (review) data, identify gaps, determine what tools and methods can be resolved, convert data to formats that can be used by these methods, confirm changes in the data if detected in advance, understand consumer needs (in which context the analyst's answer will be adopted), and clear existing assumptions. In building driving or ' abduction ', analysts are engaged in the exploration of data spaces, as well as the construction of thought models to explain the observation results.

2 deduction Inference, which examines the results of these assumptions here. (If the assumptions that occur are true, an answer to the reconstructed question can be evaluated to assert the implied correctness of the assertion hypothesis?) Reasoning may lead to the assumption that the prior formation of the argument, as different from the induced, it requires real durability. In the authentication analysis, the user may skip the induction step () and engage in a "top-to-bottom" inference evaluation. At this point, the ability of users to quickly build their information space to identify authentication features is key, compared to exploring requirements when finding results reflected in multiple complementary displays.

3 Induction inductive, inductive hypothesis test selects the most probable explanations, by finding additional instructions and optionally interpreting the permutations. The confirming phase of the analysis is a typical induction, the opposite indicator can refute the exploratory hypothesis, the alternative explanation is considered, the prejudice is evaluated, flawed and resolved. Induction is not proof of protection, future observations may alter or disprove a hypothesis, but in interactive design, it is a useful one. User goals are similar to variable, visual analysis tasks are intended to be frequent, identify the best explanations for observations, knowledge is potentially multiple interpretations, and no analysis tools can provide access to all data and all possible ways to explore data. Visual analysis tools give users a simple way to ask questions and must support these questions and their answers evolve over time.

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The science of Interaction interactive elements and the way to query the knowledge building process