Adaptive visualization of research communities

Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects

On encouraging multiple views for visualization

Visualization enables 'seeing the unseen', and provides new insight into the underlying data. However users far too easily believe or rely on a single representation of the data; this view may be a favourite method, the simplest to perform, or a method that has always been used! But, a single representation may generate a misinterpretation of the information or provide a situation where the user is missing the 'richness' of the data content! By displaying the data in multiple ways a user may understand the information through different perspectives, overcome possible misinterpretations and perform interactive investigative visualization through correlating the information between views. Thus, the use of multiple views of the same information should be encouraged. We believe the visualization system itself should actively encourage the generation of multiple views by providing appropriate tools to aid in this operation. We present and categorise issues for encouraging multiple views and provide a framework for the generation, management and manipulation of such views

Multiple Uncertainties in Time-Variant Cosmological Particle Data

Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables

An abductive process of developing interactive data visualization: a case study of market attractiveness analysis

Part 4: Business Intelligence and AnalyticsInternational audienceData visualization has been widely utilized in various scenarios in data analytics for business purposes, especially helping novice readers make sense of complex dataset with interactive functions. However, due to an insufficient theoretical understanding of the process of developing interactive functions and visual presentations, interactive data visualization tools often display all available automatic graphing functions in front of users, instead of guiding them to choose a visualization based on their demands. Thus, this paper is intended to construct a process of developing interactive visualization with a specific focus on enabling the interoperation between design and interpretation. Stemmed from organizational semiotics, an abductive process will be portrayed in this paper to interpret the process of developing interactive data visualization. Especially the interactive functions will be employed in an iterative process, where producers can be aware of and respond to readers’ information demands on semantic, pragmatic and social levels

INVISQUE: Intuitive information exploration through interactive visualization

In this paper we present INVISQUE, a novel system designed for interactive information exploration. Instead of a conventional list-style arrangement, in INVISQUE information is represented by a two-dimensional spatial canvas, with each dimension representing user-defined semantics. Search results are presented as index cards, ordered in both dimensions. Intuitive interactions are used to perform tasks such as keyword searching, results browsing, categorizing, and linking to online resources such as Google and Twitter. The interaction-based query style also naturally lends the system to different types of user input such as multi-touch gestures. As a result, INVISQUE gives users a much more intuitive and smooth experience of exploring large information spaces