Animated Edge Textures in Node-Link Diagrams: a Design Space and Initial Evaluation

International audienceNetwork edge data attributes are usually encoded using color, opacity, stroke thickness and stroke pattern, or some combination thereof. In addition to these static variables, it is also possible to animate dynamic particles flowing along the edges. This opens a larger design space of animated edge textures, featuring additional visual encodings that have potential not only in terms of visual mapping capacity but also playfulness and aesthetics. Such animated edge textures have been used in several commercial and design-oriented visualizations, but to our knowledge almost always in a relatively ad hoc manner. We introduce a design space and Web-based framework for generating animated edge textures, and report on an initial evaluation of particle properties – particle speed, pattern and frequency – in terms of visual perception

Reduce, Repeat

Presence is the quality of art that strikes a viewer; its energy is palpable. In every moment it is completely alive and in every moment after is alive once more. In 1967, critic, Michael Fried explained the importance of art’s ability to affect viewer continuously in this way and names the temporal affect of presentness as the most significant motivation of Minimalism; the essence of reduction is in this temporality. With much of the visual language of the Contemporary resting on the foundations of Minimalism, the temporal philosophy that is rooted in its vocabulary and aesthetics becomes an afterthought in the formalist discourse that Contemporary art is often framed by. To understand the reductive aesthetic comprehensively, the theory of temporal presence should be revisited. This paper is divided in two parts, the first will establish the philosophical foundations of Empty time and the second will apply concepts of Emptiness and Nothingness to reductive aesthetics. Crucial to presence are the temporal, spatial and experiential modes that are active in it. The meeting point of time and space, the current now, is where these encounters manifest but in the present moment, the movement of past-becoming-present-becoming-future occurs without our even feeling it. The ‘passing of time’ never actualises in our sensory experience, so we should cease to approach time in this way. Buddhist and Nietzschean philosophies raise concerns with this linear approach to time. To better understand the form of the present and the temporal language of reduction, I will examine the durationless model of time that shapes the sensation of experience. Buddhist, Śūnyatā and Nietzsche’s eternal return, will be examined to reconcile the disconnect between experiencing and the movement of time. The second component of the paper will examine the minimalist models of reduction, monochrome and repetition, against Fried’s essay. A clear lineage of presence becomes evident in the Contemporary’s thematic trends of ‘immaterial’ and ‘formless’, considering these examples demonstrate the legacy of reduction. To track the historical development of the minimal tradition, I will begin with Lucio Fontana (reduction) and move through to Yves Klein (monochrome) and finish with Lee Ufan (repetition). I intuitively apply the three aforementioned aesthetics to my creative practice and. Repetition plays a significant role; the demand for patience, will and perdurance is absorbing and, for me, the greatest teacher of time. The repeated gesture is an immersive exercise; it requires becoming absolutely attuned to time and Being. The mark is simultaneously the lingering anticipation of the next moment and mourning the passing of the last; it is a punctuation of Being. Time’s value is often treated as a commodity, so our desire to attain and hold it is overwhelming. It is in my practice that I can unlearn the ideas of time passing, running out, fading or eroding. The commitment to repeat infinitely eases the tension of chasing time

A stable graph layout algorithm for processes

Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high-quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high-quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high-quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges

A stable graph layout algorithm for processes

\u3cp\u3eProcess mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high-quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high-quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high-quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.\u3c/p\u3

Contour based visualization of vessel movement predictions

We present a visualization method for the interactive exploration of predicted positions of moving objects, in particular, ocean-faring vessels. Two simple prediction models, one based on similarity to historical trajectories and one on Monte Carlo simulation, are presented. The prediction models generate temporal probability density fields starting from a known situation. We use contours to visualize spatio-temporal zones of these density fields. Predictions are split into a configurable number of segments for which we render one or more contours. Users, investigating and exploring the possible development of a situation, can see where a vessel will be in the near future according to a given prediction model. Through a number of real-world use cases and a discussion with users, we show our methods can be used in monitoring traffic for collision avoidance, and detecting illegal activities, like piracy or smuggling. By applying our methods to pedestrian movements, we show that our methods can also be applied to a different domain. Keywords: situational awareness; uncertainty; vessel prediction; visualizatio

Non-overlapping aggregated multivariate glyphs for moving objects

In moving object visualization, objects and their attributes are commonly represented by glyphs on a geographic map. In areas on the map densely populated by these objects, visual clutter and occlusion of glyphs occur. We propose a method to solve this problem by partitioning the set of all objects into subsets that are each visualized using an aggregated multivariate glyph that shows the distribution of several attributes of its objects, such as heading, type and velocity. We choose the combination of subsets and glyph design such that the glyphs do not overlap and the number of subsets is approximately maximal. The partition is maintained and updated while the objects move. We use examples from the maritime domain, but our method is applicable to a wider range of dynamic data. Through a user study we find that, for a set of representative tasks, our method does not perform significantly worse than competitive visualizations with respect to correctness. Furthermore, it performs significantly better for density comparison tasks in high density data sets. We also find that the participants of the user study have a preference for our method

Interactive visualization of multivariate trajectory data with density maps

We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well

Visualization of vessel traffic

We discuss methods to visualize large amounts of object movements described with so called multivariate trajectories, which are lists of records with multiple attribute values about the state of the object. In this chapter we focus on vessel traffic as one of the examples of this kind of data. The purpose of our visualizations is to reveal what has happened over a period of time. For vessel traffic, this is beneficial for surveillance operators and analysts, since current visualizations do not give an overview of normal behavior, which is needed to find abnormally behaving ships that can be a potential threat. Our approach is inspired by the technique of kernel density estimation and smooths trajectories to obtain an overview picture with a distribution of trajectories: a density map. Using knowledge about the attributes in the data, the user can adapt these pictures by setting parameters, filters, and expressions as means for rapid prototyping, required for quickly finding other types of behavior with our visualization approach. Furthermore, density maps are computationally expensive, which we address by implementing our tools on graphics hardware. We describe different variations of our techniques and illustrate them with real-world vessel traffic data