Illustrative Parallel Coordinates

McDonnell, Kevin T.; Mueller, Klaus

doi:10.1111/j.1467-8659.2008.01239.x

Cited by 94 publications

(72 citation statements)

References 18 publications

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“…We would like to apply several parallel coordinate techniques in our system, e.g. edge clustering [44], illustrative parallel coordinates [29]. We would also like to further investigate how other multidimensional visualization forms can be closely integrated with parallel coordinates.…”

Section: Discussionmentioning

confidence: 99%

“…Parallel coordinates can further be extended into 3D through extrusion to visualize trajectories of higher dimensional dynamical systems [37], or novel axes arrangement can be used to allow the simultaneous examination of the relationships of a single dimension with many others in the data [26]. Artistic rendering techniques can augment parallel coordinates to increase comprehensibility for non-experts [29].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Scattering Points in Parallel Coordinates

Yuan

Guo

et al. 2009

IEEE Trans. Visual. Comput. Graphics

111

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Abstract-In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated Dimensional Incremental Multidimensional Scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Scattering Points in Parallel Coordinates

Yuan

Guo

et al. 2009

IEEE Trans. Visual. Comput. Graphics

111

View full text Add to dashboard Cite

show abstract

“…This has not been enforced as a constraint in the data collection phase, but surprisingly for us it supports the perception of the shape of the underlying layers nicely, without sacrificing the perception of the correct depth ordering. Figure 8 to 10 show results that have been obtained after integrating the data driven blending operator into the rendering software for illustrative parallel coordinates (IPC) by McDonnell et al [2]. Fig.…”

Section: Synthetic Test Casementioning

confidence: 99%

“…assigning a hue value to scalar data, is most often used for visual grouping and labeling of nominal data. In applications like illustrative parallel coordinates [2] and volume rendering, color mapping is combined with transparency in order to avoid occlusions. The most popular method for simulating transparency is the source-over operator from the collection of the Porter-Duff operators [3], which creates a new color C as a weighted sum of two input colors A and B,…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Hue-Preserving Color-Blending

Kühne

Giesen

Zhang

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced methods have also been proposed, but the problem is still far from being solved. Here we present an alternative to these existing methods specifically devised to avoid false colors and preserve visual depth ordering. Our approach is data driven and follows the recently formulated knowledge-assisted visualization (KAV) paradigm. Preference data, that have been gathered in web-based user surveys, are used to train a support-vector machine model for automatically predicting an optimized hue-preserving blending. We have applied the resulting model to both volume rendering and a specific information visualization technique, illustrative parallel coordinate plots. Comparative renderings show a significant improvement over previous approaches in the sense that false colors are completely removed and important properties such as depth ordering and blending vividness are better preserved. Due to the generality of the defined data-driven blending operator, it can be easily integrated also into other visualization frameworks.

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“…Previously, we described an illustrative information visualization framework for high-dimensional data that used parallel coordinates. 7 In this framework, we employed k-means clustering, mean, standard deviation, and correlation for analysis, and we used the results to determine visual salience. As illustrative style elements, we employed halos, shadows, color, shading, and filled contours.…”

Section: Brevity By Abstractionmentioning

confidence: 99%

Can Computers Master the Art of Communication?: A Focus on Visual Analytics

Mueller

Garg

Nam

et al. 2011

IEEE Comput. Grap. Appl.

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Visual analytics seeks to conduct a discourse with the user through images, to stimulate curiosity and a penchant to decipher the unknown. Figure 1 depicts our view of the visual analytics process. The computer supports the user in this interactive analytical reasoning, constructing a formal model of the given data, with the end product being formatted knowledge constituting insight.Yet, validation and refi nement of this computational model of insight can occur only in the human domain expert's mind, bringing to bear possibly unformatted knowledge as well as intuition and creative thought. So, it's left to this human user to guide the computer in the formalization (learning) of more sophisticated models that capture what the human desires and what the computer currently believes about the data domain, perhaps with an associated confi dence level. In visual analytics, the computer uses images and text (and possibly sound and haptics) to exchange information with the user about its view of the domain model.Obviously, the better a communicator the computer is, the more assistance it will elicit from the user to help it refi ne the model. This in turn leads to this article's topic-the need for the computer to master the art of interpersonal communication-that is, communication between it and the human analyst. The Elements of Interpersonal CommunicationObviously, communication is present in many domains, not just in human behavior. Communication protocols are part of many human-made systems, such as computing and telecommunication, and they follow similar defi nitions. We focus here on human behavior because we aim for the computer to collaborate with the human user.The interpersonal-communication protocol 1 (see Figure 2) always includes The interpersonal-communication framework has three components. Direct channels encompass information that the sender directly controls; they're easily recognized by the receiver. Indirect channels aren't always under the sender's direct control and are usually recognized subconsciously by the receiver. The context is the conditions surrounding the communication from which the receiver can derive the message's meaning.Communicators use intonation or pitch to emphasize words and passages. Brevity or economy of words leads to clear, effective presentations, whereas an aesthetic choice of words (good storytelling) can generate more interest, attention, and even fascination. Finally, personalization of word choice can target a specifi c receiver, just as the word choice can indicate a sender's identity.Clearly, some people are more eloquent in these matters than others; the same is true for human-

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Illustrative Parallel Coordinates

Cited by 94 publications

References 18 publications

Scattering Points in Parallel Coordinates

Scattering Points in Parallel Coordinates

A Data-Driven Approach to Hue-Preserving Color-Blending

Can Computers Master the Art of Communication?: A Focus on Visual Analytics

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