Knowledge Generation Model for Visual Analytics

Abstract

Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.

Structuring visual exploratory analysis of skill demand

“…However, an open challenge, especially where the backgrounds and technical capability of target users vary significantly, is corresponding variation in visual literacy [1,29,31]. To address this, we follow a userand task-centred approach that uses ontologies to abstract and aggregate dynamic, heterogeneous data into structured information spaces.…”

“…We extend van Wijk's visual analytics model [2] to define our methodology, illustrated in Fig. 1, that employs (interactive) visual analytics, relying on the human in the data exploration and analysis loop, to obtain an understanding, first, of data content [see also 1,26,29,31]. Working from this we iteratively build, verify and refine a model of our target users, their tasks and the resources required to work toward their end goals, as we widen the scope of our analysis to include new data.…”

“…Visual analytics provides a valuable tool that complements advanced human perception and analytical ability with automated data processing and mining. This lowers human cognitive load in complex data analysis while improving the recognition of patterns and information hidden within data [1,2,4,28,29], support especially valuable in the analysis of complex, high-dimensional data.…”

“…Designing effective support for complex scenarios and data typically involves the exploration of the impact and use of multiple sub-components [4,12,26,27,29,32], following a process that may involve any or all of the following in an iterative cycle:…”

Visual Analytics for Network Security and Critical Infrastructures

“…This approach follows the Shneiderman's visual information-seeking mantra: "Overview first, zoom and filter, then details-on-demand" [11]. By applying this mantra in the visual analysis domain, Sacha et al [10] proposed an approach enabling the visual analytic theories to go beyond the inclusion of the human factor in the process, to the theory where human is a part of the loop [3].…”