DevLan/vispubs huggingface dataset:
Towards High-dimensional Data Analysis in Air Quality Research
Authors: Daniel Engel, Mathias Hummel, F. Hoepel, Keith Bein, Anthony S. Wexler, Christoph Garth, Bernd Hamann, Hans Hagen
Published at EuroVis, 2013

Analysis of chemical constituents from mass spectrometry of aerosols involves non‐negative matrix factorization, an approximation of high‐dimensional data in lower‐dimensional space. The associated optimization problem is non‐convex, resulting in crude approximation errors that are not accessible to scientists. To address this shortcoming, we introduce a new methodology for user‐guided error‐aware data factorization that entails an assessment of the amount of information contributed by each dimension of the approximation, an effective combination of visualization techniques to highlight, filter, and analyze error features, as well as a novel means to interactively refine factorizations. A case study and the domain‐expert feedback provided by the collaborating atmospheric scientists illustrate that our method effectively communicates errors of such numerical optimization results and facilitates the computation of high‐quality data factorizations in a simple and intuitive manner.
Towards Multifield Scalar Topology Based on Pareto Optimality
Authors: Lars Huettenberger, Christian Heine 0002, Hamish A. Carr, Gerik Scheuermann, Christoph Garth
Published at EuroVis, 2013

How can the notion of topological structures for single scalar fields be extended to multifields? In this paper we propose a definition for such structures using the concepts of Pareto optimality and Pareto dominance. Given a set of piecewise‐linear, scalar functions over a common simplical complex of any dimension, our method finds regions of “consensus” among single fields’ critical points and their connectivity relations. We show that our concepts are useful to data analysis on real‐world examples originating from fluid‐flow simulations; in two cases where the consensus of multiple scalar vortex predictors is of interest and in another case where one predictor is studied under different simulation parameters. We also compare the properties of our approach with current alternatives.
TrajectoryLenses - A Set-based Filtering and Exploration Technique for Long-term Trajectory Data
Authors: Robert Krüger, Dennis Thom, Michael Wörner 0001, Harald Bosch, Thomas Ertl
Published at EuroVis, 2013

The visual analysis of spatiotemporal movement is a challenging task. There may be millions of routes of different length and shape with different origin and destination, extending over a long time span. Furthermore there can be various correlated attributes depending on the data domain, e.g. engine measurements for mobility data or sensor data for animal tracking. Visualizing such data tends to produce cluttered and incomprehensible images that need to be accompanied by sophisticated filtering methods. We present TrajectoryLenses, an interaction technique that extends the exploration lens metaphor to support complex filter expressions and the analysis of long time periods. Analysts might be interested only in movements that occur in a given time range, traverse a certain region, or end at a given area of interest (AOI). Our lenses can be placed on an interactive map to identify such geospatial AOIs. They can be grouped with set operations to create powerful geospatial queries. For each group of lenses, users can access aggregated data for different attributes like the number of matching movements, covered time, or vehicle performance. We demonstrate the applicability of our technique on a large, real‐world dataset of electric scooter tracks spanning a 2‐year period.
User-driven Feature Space Transformation
Authors: Gladys M. H. Mamani, Francisco M. Fatore, Luis Gustavo Nonato, Fernando Vieira Paulovich
Published at EuroVis, 2013

Interactive visualization systems for exploring and manipulating high‐dimensional feature spaces have experienced a substantial progress in the last few years. State‐of‐art methods rely on solid mathematical and computational foundations that enable sophisticated and flexible interactive tools. Current methods are even capable of modifying data attributes during interaction, highlighting regions of potential interest in the feature space, and building visualizations that bring out the relevance of attributes. However, those methodologies rely on complex and non‐intuitive interfaces that hamper the free handling of the feature spaces. Moreover, visualizing how neighborhood structures are affected during the space manipulation is also an issue for existing methods. This paper presents a novel visualization‐assisted methodology for interacting and transforming data attributes embedded in feature spaces. The proposed approach relies on a combination of multidimensional projections and local transformations to provide an interactive mechanism for modifying attributes. Besides enabling a simple and intuitive visual layout, our approach allows the user to easily observe the changes in neighborhood structures during interaction. The usefulness of our methodology is shown in an application geared to image retrieval.
Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields
Authors: Nivan Ferreira, James T. Klosowski, Carlos Eduardo Scheidegger, Cláudio T. Silva
Published at EuroVis, 2013

Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. In this paper, we introduce a novel trajectory clustering technique whose central idea is to use vector fields to induce a notion of similarity between trajectories, letting the vector fields themselves define and represent each cluster. We present an efficient algorithm to find a locally optimal clustering of trajectories into vector fields, and demonstrate how vector‐field k‐means can find patterns missed by previous methods. We present experimental evidence of its effectiveness and efficiency using several datasets, including historical hurricane data, GPS tracks of people and vehicles, and anonymous cellular radio handoffs from a large service provider.
Vessel Visualization using Curvicircular Feature Aggregation
Authors: Gabriel Mistelbauer, Anca Morar, Andrej Varchola, Rüdiger Schernthaner, Ivan Baclija, Arnold Köchl, Armin Kanitsar, Stefan Bruckner, M. Eduard Gröller
Published at EuroVis, 2013

Radiological investigations are common medical practice for the diagnosis of peripheral vascular diseases. Existing visualization methods such as Curved Planar Reformation (CPR) depict calcifications on vessel walls to determine if blood is still able to flow. While it is possible with conventional CPR methods to examine the whole vessel lumen by rotating around the centerline of a vessel, we propose Curvicircular Feature Aggregation (CFA), which aggregates these rotated images into a single view. By eliminating the need for rotation, vessels can be investigated by inspecting only one image. This method can be used as a guidance and visual analysis tool for treatment planning. We present applications of this technique in the medical domain and give feedback from radiologists.
VisRuption: Intuitive and Efficient Visualization of Temporal Airline Disruption Data
Authors: Paul Rosenthal, Linda Pfeiffer, Nicholas Hugo Müller, Peter Ohler
Published at EuroVis, 2013

The operation of an airline is a very complex task and disruptions to the planned operation can occur on very short notice. Already a small disruption like a delay of some minutes can cost the airline a tremendous amount of money. Hence, it is crucial to proactively control all operations of the airline and efficiently prioritize and handle disruptions. Due to the complex setting and the need for ad hoc decisions this task can only be carried out by human operation controllers. In the field of airline operations control there exists already a vast variety of different software in productive use. We analyze the different approaches from two of the market leaders and identify problematic design choices. We take into account this analysis and develop a set of rules for an intuitive visualization of airline disruption data. Finally, we introduce our tool for visualizing such data which complies to these rules. The visualization enables the user to gain a fast overview over the current problem situation and to intuitively prioritize different problems and problem hierarchies. The efficiency of the design is evaluated with the help of a user study which shows that the new system significantly outperforms the current state of the art.
Visual Analysis of Set Relations in a Graph
Authors: Panpan Xu, Fan Du, Nan Cao, Conglei Shi, Hong Zhou 0004, Huamin Qu
Published at EuroVis, 2013

Many applications can be modeled as a graph with additional attributes attached to the nodes. For example, a graph can be used to model the relationship of people in a social media website or a bibliographical dataset. Meanwhile, additional information is often available, such as the topics people are interested in and the music they listen to. Based on this additional information, different set relationships may exist among people. Revealing the set relationships in a network can help people gain social insight and better understand their roles within a community. In this paper, we present a visualization system for exploring set relations in a graph. Our system is designed to reveal three different relationships simultaneously: the social relationship of people, the set relationship among people's items of interest, and the similarity relationship of the items. We propose two novel visualization designs: a) a glyph‐based visualization to reveal people's set relationships in the context of their social networks; b) an integration of visual links and a contour map to show people and their items of interest which are clustered into different groups. The effectiveness of the designs has been demonstrated by the case studies on two representative datasets including one from a social music service website and another from an academic collaboration network.
Visual Explanation of the Complexity in Julia Sets
Authors: Okke Schrijvers, Jarke J. van Wijk
Published at EuroVis, 2013

Julia sets based on quadratic polynomials have a very simple definition, yet a highly intricate shape. Our contribution is to provide a visual explanation for this complexity. To this end we show the construction of Julia sets as a dynamic process, in contrast to showing just a static image of the set itself. Our method is based on the Inverse Iteration Method (IIM). We start with a disk, which is successively distorted. The crucial step is to show an animation of the effect of taking a root of a subset of the complex plane. We present four different approaches for this, using a Riemann surface, a corkscrew, a fan, and disks as metaphors. We packaged our results in an interactive tool with a simple interface, such that everybody can view and inspect these for different Julia sets. The results are useful for teaching complex analysis, promoting mathematics, entertainment, and, above all, as a visual explanation for the complexity of Julia sets.
Visualizing Interchange Patterns in Massive Movement Data
Authors: Wei Zeng 0004, Chi-Wing Fu, Stefan Müller Arisona, Huamin Qu
Published at EuroVis, 2013

Massive amount of movement data, such as daily trips made by millions of passengers in a city, are widely available nowadays. They are a highly valuable means not only for unveiling human mobility patterns, but also for assisting transportation planning, in particular for metropolises around the world. In this paper, we focus on a novel aspect of visualizing and analyzing massive movement data, i.e., the interchange pattern, aiming at revealing passenger redistribution in a traffic network. We first formulate a new model of circos figure, namely the interchange circos diagram, to present interchange patterns at a junction node in a bundled fashion, and optimize the color assignments to respect the connections within and between junction nodes. Based on this, we develop a family of visual analysis techniques to help users interactively study interchange patterns in a spatiotemporal manner: 1) multi‐spatial scales: from network junctions such as train stations to people flow across and between larger spatial areas; and 2) temporal changes of patterns from different times of the day. Our techniques have been applied to real movement data consisting of hundred thousands of trips, and we present also two case studies on how transportation experts worked with our interface.
Visualizing Large-scale Parallel Communication Traces Using a Particle Animation Technique
Authors: Carmen Sigovan, Chris Muelder, Kwan-Liu Ma
Published at EuroVis, 2013

Large‐scale scientific simulations require execution on parallel computing systems in order to yield useful results in a reasonable time frame. But parallel execution adds communication overhead. The impact that this overhead has on performance may be difficult to gauge, as parallel application behaviors are typically harder to understand than the sequential types. We introduce an animation‐based interactive visualization technique for the analysis of communication patterns occurring in parallel application execution. Our method has the advantages of illustrating the dynamic communication patterns in the system as well as a static image of MPI (Message Passing Interface) utilization history. We also devise a data streaming mechanism that allows for the exploration of very large data sets. We demonstrate the effectiveness of our approach scaling up to 16 thousand processes using a series of trace data sets of ScaLAPACK matrix operations functions.
Visualizing Motional Correlations in Molecular Dynamics using Geometric Deformations
Authors: Matthew Fioravante, Adam Shook, Ian Thorpe, Penny Rheingans
Published at EuroVis, 2013

In macromolecules, an allosteric effect is said to occur when a change at one site of a molecule affects a distant site. Understanding these allosteric effects can be important for understanding how the functions of complex molecules such as proteins are regulated. One potential application of this knowledge is the development of small molecules that alter the function of proteins involved in diseases. Studying motional correlation can help researchers to discover how a change at a source site affects the target site and thus how allosteric ligands that could serve as drugs are able to exert their therapeutic effects. By improving our ability to analyze these correlated relationships, it may be possible to develop new medications to combat deadly diseases such as Hepatitis C. We present four visual techniques which represent motional correlation on rendered three‐dimensional molecular models, providing new ways to view clusters of correlated residues and paths of allosteric interactions. These techniques give us a new way of investigating the presence of motional correlations in complex molecules. We compare each of these techniques to determine which are the most useful for representing motional correlations.
Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields
Authors: Bei Wang 0001, Paul Rosen 0001, Primoz Skraba, Harsh Bhatia, Valerio Pascucci
Published at EuroVis, 2013

Analyzing critical points and their temporal evolutions plays a crucial role in understanding the behavior of vector fields. A key challenge is to quantify the stability of critical points: more stable points may represent more important phenomena or vice versa. The topological notion of robustness is a tool which allows us to quantify rigorously the stability of each critical point. Intuitively, the robustness of a critical point is the minimum amount of perturbation necessary to cancel it within a local neighborhood, measured under an appropriate metric. In this paper, we introduce a new analysis and visualization framework which enables interactive exploration of robustness of critical points for both stationary and time‐varying 2D vector fields. This framework allows the end‐users, for the first time, to investigate how the stability of a critical point evolves over time. We show that this depends heavily on the global properties of the vector field and that structural changes can correspond to interesting behavior. We demonstrate the practicality of our theories and techniques on several datasets involving combustion and oceanic eddy simulations and obtain some key insights regarding their stable and unstable features.
ViviSection: Skeleton-based Volume Editing
Authors: Alexey Karimov, Gabriel Mistelbauer, Johanna Schmidt, Peter Mindek, Elisabeth Schmidt, Timur Sharipov, Stefan Bruckner, M. Eduard Gröller
Published at EuroVis, 2013

Volume segmentation is important in many applications, particularly in the medical domain. Most segmentation techniques, however, work fully automatically only in very restricted scenarios and cumbersome manual editing of the results is a common task. In this paper, we introduce a novel approach for the editing of segmentation results. Our method exploits structural features of the segmented object to enable intuitive and robust correction and verification. We demonstrate that our new approach can significantly increase the segmentation quality even in difficult cases such as in the presence of severe pathologies.
Binocular cursor: enabling selection on transparent displays troubled by binocular parallax
Authors: Joon Hyub Lee, Seok-Hyung Bae
Published at CHI, 2013

Binocular parallax is a problem for any interaction system that has a transparent display and objects behind it, as users will see duplicated and overlapped images. In this note, we propose a quantitative measure called Binocular Selectability Discriminant (BSD) to predict the ability of the user to perform selection task in such a setup. In addition, we propose a technique called Binocular Cursor (BC) which takes advantage of this duplicating and overlapping phenomenon, rather than being hampered by it, to resolve binocular selection ambiguity by visualizing the correct selection point. An experiment shows that selection with BC is not slower than monocular selection, and that it can be significantly more precise, depending on the design of BC.
Canyon: providing location awareness of multiple moving objects in a detail view on large displays
Authors: Alexandra Ion, Yu-Ling Betty Chang, Michael Haller, Mark S. Hancock, Stacey D. Scott
Published at CHI, 2013

Overview+Detail interfaces can be used to examine the details of complex data while retaining the data's overall context. Dynamic data introduce challenges for these interfaces, however, as moving objects may exit the detail view, as well as a person's field of view if they are working at a large interactive surface. To address this "off-view" problem, we propose a new information visualization technique, called Canyon. This technique attaches a small view of an off-view object, including some surrounding context, to the external boundary of the detail view. The area between the detail view and the region containing the off-view object is virtually "folded" to conserve space. A comparison study was conducted contrasting the benefits and limitations of Canyon to an established technique, called Wedge. Canyon was more accurate across a number of tasks, especially more complex tasks, and was comparably efficient.
Contextifier: automatic generation of annotated stock visualizations
Authors: Jessica Hullman, Nicholas Diakopoulos, Eytan Adar
Published at CHI, 2013

Online news tools - for aggregation, summarization and automatic generation - are an area of fruitful development as reading news online becomes increasingly commonplace. While textual tools have dominated these developments, annotated information visualizations are a promising way to complement articles based on their ability to add context. But the manual effort required for professional designers to create thoughtful annotations for contextualizing news visualizations is difficult to scale. We describe the design of Contextifier, a novel system that automatically produces custom, annotated visualizations of stock behavior given a news article about a company. Contextifier's algorithms for choosing annotations is informed by a study of professionally created visualizations and takes into account visual salience, contextual relevance, and a detection of key events in the company's history. In evaluating our system we find that Contextifier better balances graphical salience and relevance than the baseline.
Direct manipulation video navigation in 3D
Authors: Cuong Nguyen 0003, Yuzhen Niu, Feng Liu 0015
Published at CHI, 2013

Direct Manipulation Video Navigation (DMVN) systems allow a user to navigate a video by dragging an object along its motion trajectory. These systems have been shown effective for space-centric video browsing. Their performance, however, is often limited by temporal ambiguities in a video with complex motion, such as recurring motion, self-intersecting motion, and pauses. The ambiguities come from reducing the 3D spatial-temporal motion (x, y, t) to the 2D spatial motion (x, y) in visualizing the motion and dragging the object. In this paper, we present a 3D DMVN system that maps the spatial-temporal motion (x, y, t) to 3D space (x, y, z) by mapping time t to depth z, visualizes the motion and video frame in 3D, and allows to navigate the video by spatial-temporally manipulating the object in 3D. We show that since our 3D DMVN system preserves all the motion information, it resolves the temporal ambiguities and supports intuitive navigation on challenging videos with complex motion.
Effects of visualization and note-taking on sensemaking and analysis
Authors: Nitesh Goyal, Gilly Leshed, Susan R. Fussell
Published at CHI, 2013

Many sophisticated tools have been developed to help analysts detect patterns in large datasets, but the value of these tools' individual features is rarely tested. In an experiment in which participants played detectives solving homicides, we tested the utility of a visualization of data links and a notepad for collecting and organizing annotations. The visualization significantly improved participants' ability to solve the crime whereas the notepad did not. Having both features available provided no benefit over having just the visualization. The results raise questions about the potential constraints on the usefulness of intelligence analysis tools.
Evaluating the efficiency of physical visualizations
Authors: Yvonne Jansen, Pierre Dragicevic, Jean-Daniel Fekete
Published at CHI, 2013

Data sculptures are an increasingly popular form of physical visualization whose purposes are essentially artistic, communicative or educational. But can physical visualizations help carry out actual information visualization tasks? We present the first infovis study comparing physical to on-screen visualizations. We focus on 3D visualizations, as these are common among physical visualizations but known to be problematic on computers. Taking 3D bar charts as an example, we show that moving visualizations to the physical world can improve users' efficiency at information retrieval tasks. In contrast, augmenting on-screen visualizations with stereoscopic rendering alone or with prop-based manipulation was of limited help. The efficiency of physical visualizations seems to stem from features that are unique to physical objects, such as their ability to be touched and their perfect visual realism. These findings provide empirical motivation for current research on fast digital fabrication and self-reconfiguring interfaces.