Data, Information, and Knowledge in Visualization

Abstract-VisualAnalytics is "the science of analytical reasoning facilitated by visual interactive interfaces" [70]. The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on humanand machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

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“…This is the affordance of domain knowledge. In their 2009 article on KnowledgeAssisted Visualization [13], Chen et al argue:…”

Section: Domain Knowledgementioning

confidence: 99%

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

Crouser

Chang

2012

IEEE Trans. Visual. Comput. Graphics

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“…A subsequent visualization of the inter-and intra-cluster relationships, along with iconic representation of the underlying patterns, then optionally allows users to get a feel for the semantics of their data. This enables an information-assisted visualization interface [5], in which users, possibly experts, would select visualization method and parameters based on this explanatory data property illustration.…”

Section: Previous Workmentioning

confidence: 99%

“…Knowledge-assisted visualization (KAV) [5] seeks to augment visualization methods as well as data with expert knowledge, in order to make navigation through visualization parameters spaces easier for users unfamiliar with a given method or data visualization in general. Major challenges here are how this knowledge is collected, selected and stored and how it is indexed by data and task.…”

Section: Introductionmentioning

confidence: 99%

A high-dimensional feature clustering approach to support knowledge-assisted visualization

Nam

Maurer

Mueller

2009

Computers & Graphics

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a b s t r a c tThe ever-growing arsenal of methods and parameters available for data visualization can be daunting to the casual user and even to domain experts. Furthermore, comprehensive expertise is often not available in a centralized venue, but distributed over sub-communities. As a means to overcome this inherent problem, efforts have begun to store visualization expertise directly with the visualization method and possibly the dataset, to then be utilized for user guidance in the data visualization, suggesting to the user both the visualization method and its best parameters for the data and task at hand. While this is certainly an immensely useful and promising development, one requirement remains -the matching of a newly acquired dataset with the appropriate segment of the library storing the expert knowledge. This requires one to detect and recognize the dataset's category at some level of granularity and then use this information as a library index. We describe a possible framework for accomplishing the first stage of this process, namely the data categorization, using data classification via a rich set of feature vectors sufficiently sensitive to detect critical variations. We demonstrate the utility of our framework by ways of a set of medical and computational datasets and visualize the resulting categorization as a layout in 2D.

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“…Frequently, these techniques are only used to present some observation found through prior analysis. However, data visualization is not limited to this capacity and can be useful during the preliminary analysis phase by providing the analyst with other techniques to explore the data, and can often be done at lower costs than a traditional data-mining effort might require [4,5]. By representing the data in a visual manner, we can enable the analyst to focus on tasks that humans are ideally suited for -anomaly and context identification, and pattern recognition -while allowing the computer to focus on tasks that it is suited for -large data processing.…”

Section: Introductionmentioning

confidence: 99%