DevLan/vispubs huggingface dataset:
Using Interactive Visual Reasoning to Support Sense-Making: Implications for Design
Authors: Neesha Kodagoda, Simon Attfield, B. L. William Wong, Chris Rooney, Sharmin (Tinni) Choudhury
Published at VAST, 2013

This research aims to develop design guidelines for systems that support investigators and analysts in the exploration and assembly of evidence and inferences. We focus here on the problem of identifying candidate 'influencers' within a community of practice. To better understand this problem and its related cognitive and interaction needs, we conducted a user study using a system called INVISQUE (INteractive Visual Search and QUery Environment) loaded with content from the ACM Digital Library. INVISQUE supports search and manipulation of results over a freeform infinite 'canvas'. The study focuses on the representations user create and their reasoning process. It also draws on some pre-established theories and frameworks related to sense-making and cognitive work in general, which we apply as a 'theoretical lenses' to consider findings and articulate solutions. Analysing the user-study data in the light of these provides some understanding of how the high-level problem of identifying key players within a domain can translate into lower-level questions and interactions. This, in turn, has informed our understanding of representation and functionality needs at a level of description which abstracts away from the specifics of the problem at hand to the class of problems of interest. We consider the study outcomes from the perspective of implications for design.
UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization
Authors: Jaegul Choo, Changhyun Lee, Chandan K. Reddy, Haesun Park
Published at VAST, 2013

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.
VAICo: Visual Analysis for Image Comparison
Authors: Johanna Schmidt, M. Eduard Gröller, Stefan Bruckner
Published at VAST, 2013

Scientists, engineers, and analysts are confronted with ever larger and more complex sets of data, whose analysis poses special challenges. In many situations it is necessary to compare two or more datasets. Hence there is a need for comparative visualization tools to help analyze differences or similarities among datasets. In this paper an approach for comparative visualization for sets of images is presented. Well-established techniques for comparing images frequently place them side-by-side. A major drawback of such approaches is that they do not scale well. Other image comparison methods encode differences in images by abstract parameters like color. In this case information about the underlying image data gets lost. This paper introduces a new method for visualizing differences and similarities in large sets of images which preserves contextual information, but also allows the detailed analysis of subtle variations. Our approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this process are then presented in an interactive web application which allows users to rapidly explore the space of differences and drill-down on particular features. We demonstrate the flexibility of our approach by applying it to multiple distinct domains.
Vis4Heritage: Visual Analytics Approach on Grotto Wall Painting Degradations
Authors: Jiawan Zhang, Kai Kang, Dajian Liu, Ye Yuan, E. Yanli
Published at VAST, 2013

For preserving the grotto wall paintings and protecting these historic cultural icons from the damage and deterioration in nature environment, a visual analytics framework and a set of tools are proposed for the discovery of degradation patterns. In comparison with the traditional analysis methods that used restricted scales, our method provides users with multi-scale analytic support to study the problems on site, cave, wall and particular degradation area scales, through the application of multidimensional visualization techniques. Several case studies have been carried out using real-world wall painting data collected from a renowned World Heritage site, to verify the usability and effectiveness of the proposed method. User studies and expert reviews were also conducted through by domain experts ranging from scientists such as microenvironment researchers, archivists, geologists, chemists, to practitioners such as conservators, restorers and curators.
Visual Analysis of Higher-Order Conjunctive Relationships in Multidimensional Data Using a Hypergraph Query System
Authors: Rachel Shadoan, Chris E. Weaver
Published at VAST, 2013

Visual exploration and analysis of multidimensional data becomes increasingly difficult with increasing dimensionality. We want to understand the relationships between dimensions of data, but lack flexible techniques for exploration beyond low-order relationships. Current visual techniques for multidimensional data analysis focus on binary conjunctive relationships between dimensions. Recent techniques, such as cross-filtering on an attribute relationship graph, facilitate the exploration of some higher-order conjunctive relationships, but require a great deal of care and precision to do so effectively. This paper provides a detailed analysis of the expressive power of existing visual querying systems and describes a more flexible approach in which users can explore n-ary conjunctive inter- and intra- dimensional relationships by interactively constructing queries as visual hypergraphs. In a hypergraph query, nodes represent subsets of values and hyperedges represent conjunctive relationships. Analysts can dynamically build and modify the query using sequences of simple interactions. The hypergraph serves not only as a query specification, but also as a compact visual representation of the interactive state. Using examples from several domains, focusing on the digital humanities, we describe the design considerations for developing the querying system and incorporating it into visual analysis tools. We analyze query expressiveness with regard to the kinds of questions it can and cannot pose, and describe how it simultaneously expands the expressiveness of and is complemented by cross-filtering.
Visual Analysis of Topic Competition on Social Media
Authors: Panpan Xu, Yingcai Wu, Enxun Wei, Tai-Quan Peng, Shixia Liu, Jonathan J. H. Zhu, Huamin Qu
Published at VAST, 2013

How do various topics compete for public attention when they are spreading on social media? What roles do opinion leaders play in the rise and fall of competitiveness of various topics? In this study, we propose an expanded topic competition model to characterize the competition for public attention on multiple topics promoted by various opinion leaders on social media. To allow an intuitive understanding of the estimated measures, we present a timeline visualization through a metaphoric interpretation of the results. The visual design features both topical and social aspects of the information diffusion process by compositing ThemeRiver with storyline style visualization. ThemeRiver shows the increase and decrease of competitiveness of each topic. Opinion leaders are drawn as threads that converge or diverge with regard to their roles in influencing the public agenda change over time. To validate the effectiveness of the visual analysis techniques, we report the insights gained on two collections of Tweets: the 2012 United States presidential election and the Occupy Wall Street movement.
Visual Analytics for Model Selection in Time Series Analysis
Authors: Markus Bögl, Wolfgang Aigner, Peter Filzmoser, Tim Lammarsch, Silvia Miksch, Alexander Rind
Published at VAST, 2013

Model selection in time series analysis is a challenging task for domain experts in many application areas such as epidemiology, economy, or environmental sciences. The methodology used for this task demands a close combination of human judgement and automated computation. However, statistical software tools do not adequately support this combination through interactive visual interfaces. We propose a Visual Analytics process to guide domain experts in this task. For this purpose, we developed the TiMoVA prototype that implements this process based on user stories and iterative expert feedback on user experience. The prototype was evaluated by usage scenarios with an example dataset from epidemiology and interviews with two external domain experts in statistics. The insights from the experts' feedback and the usage scenarios show that TiMoVA is able to support domain experts in model selection tasks through interactive visual interfaces with short feedback cycles.
Visual Analytics for Multimodal Social Network Analysis: A Design Study with Social Scientists
Authors: Sohaib Ghani, Bum Chul Kwon, Seungyoon Lee, Ji Soo Yi, Niklas Elmqvist
Published at VAST, 2013

Social network analysis (SNA) is becoming increasingly concerned not only with actors and their relations, but also with distinguishing between different types of such entities. For example, social scientists may want to investigate asymmetric relations in organizations with strict chains of command, or incorporate non-actors such as conferences and projects when analyzing coauthorship patterns. Multimodal social networks are those where actors and relations belong to different types, or modes, and multimodal social network analysis (mSNA) is accordingly SNA for such networks. In this paper, we present a design study that we conducted with several social scientist collaborators on how to support mSNA using visual analytics tools. Based on an openended, formative design process, we devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw. We then used the tool in a qualitative evaluation involving five social scientists whose feedback informed a second design phase that incorporated additional network metrics. Finally, we conducted a second qualitative evaluation with our social scientist collaborators that provided further insights on the utility of the PNLBs representation and the potential of visual analytics for mSNA.
Visual Analytics for Spatial Clustering: Using a Heuristic Approach for Guided Exploration
Authors: Eli Packer, Peter Bak, Mikko Nikkilä, Valentin Polishchuk, Harold J. Ship
Published at VAST, 2013

We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data.
Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips
Authors: Nivan Ferreira, Jorge Poco, Huy T. Vo, Juliana Freire, Cláudio T. Silva
Published at VAST, 2013

As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.
Visual Traffic Jam Analysis Based on Trajectory Data
Authors: Zuchao Wang, Min Lu 0002, Xiaoru Yuan, Junping Zhang, Huub van de Wetering
Published at VAST, 2013

In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.
A Lightweight Tangible 3D Interface for Interactive Visualization of Thin fiber Structures
Authors: Bret Jackson, Tung Yuen Lau, David Schroeder, Kimani C. Toussaint, Daniel F. Keefe
Published at SciVis, 2013

We present a prop-based, tangible interface for 3D interactive visualization of thin fiber structures. These data are commonly found in current bioimaging datasets, for example second-harmonic generation microscopy of collagen fibers in tissue. Our approach uses commodity visualization technologies such as a depth sensing camera and low-cost 3D display. Unlike most current uses of these emerging technologies in the games and graphics communities, we employ the depth sensing camera to create a fish-tank sterePoscopic virtual reality system at the scientist's desk that supports tracking of small-scale gestures with objects already found in the work space. We apply the new interface to the problem of interactive exploratory visualization of three-dimensional thin fiber data. A critical task for the visual analysis of these data is understanding patterns in fiber orientation throughout a volume.The interface enables a new, fluid style of data exploration and fiber orientation analysis by using props to provide needed passive-haptic feedback, making 3D interactions with these fiber structures more controlled. We also contribute a low-level algorithm for extracting fiber centerlines from volumetric imaging. The system was designed and evaluated with two biophotonic experts who currently use it in their lab. As compared to typical practice within their field, the new visualization system provides a more effective way to examine and understand the 3D bioimaging datasets they collect.
A Multi-Criteria Approach to Camera Motion Design for Volume Data Animation
Authors: Wei-Hsien Hsu, Yubo Zhang 0001, Kwan-Liu Ma
Published at SciVis, 2013

We present an integrated camera motion design and path generation system for building volume data animations. Creating animations is an essential task in presenting complex scientific visualizations. Existing visualization systems use an established animation function based on keyframes selected by the user. This approach is limited in providing the optimal in-between views of the data. Alternatively, computer graphics and virtual reality camera motion planning is frequently focused on collision free movement in a virtual walkthrough. For semi-transparent, fuzzy, or blobby volume data the collision free objective becomes insufficient. Here, we provide a set of essential criteria focused on computing camera paths to establish effective animations of volume data. Our dynamic multi-criteria solver coupled with a force-directed routing algorithm enables rapid generation of camera paths. Once users review the resulting animation and evaluate the camera motion, they are able to determine how each criterion impacts path generation. In this paper, we demonstrate how incorporating this animation approach with an interactive volume visualization system reduces the effort in creating context-aware and coherent animations. This frees the user to focus on visualization tasks with the objective of gaining additional insight from the volume data.
A Systematic Review on the Practice of Evaluating Visualization
Authors: Tobias Isenberg 0001, Petra Isenberg, Jian Chen 0006, Michael Sedlmair, Torsten Möller
Published at SciVis, 2013

We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by Lam et al. [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90% of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity.
Acuity-Driven Gigapixel Visualization
Authors: Charilaos Papadopoulos, Arie E. Kaufman
Published at SciVis, 2013

We present a framework for acuity-driven visualization of super-high resolution image data on gigapixel displays. Tiled display walls offer a large workspace that can be navigated physically by the user. Based on head tracking information, the physical characteristics of the tiled display and the formulation of visual acuity, we guide an out-of-core gigapixel rendering scheme by delivering high levels of detail only in places where it is perceivable to the user. We apply this principle to gigapixel image rendering through adaptive level of detail selection. Additionally, we have developed an acuity-driven tessellation scheme for high-quality Focus-and-Context (F+C) lenses that significantly reduces visual artifacts while accurately capturing the underlying lens function. We demonstrate this framework on the Reality Deck, an immersive gigapixel display. We present the results of a user study designed to quantify the impact of our acuity-driven rendering optimizations in the visual exploration process. We discovered no evidence suggesting a difference in search task performance between our framework and naive rendering of gigapixel resolution data, while realizing significant benefits in terms of data transfer overhead. Additionally, we show that our acuity-driven tessellation scheme offers substantially increased frame rates when compared to naive pre-tessellation, while providing indistinguishable image quality.
Adaptive Refinement of the Flow Map Using Sparse Samples
Authors: Samer S. Barakat, Xavier Tricoche
Published at SciVis, 2013

We present a new efficient and scalable method for the high quality reconstruction of the flow map from sparse samples. The flow map describes the transport of massless particles along the flow. As such, it is a fundamental concept in the analysis of transient flow phenomena and all so-called Lagrangian flow visualization techniques require its approximation. The flow map is generally obtained by integrating a dense 1D, 2D, or 3D set of particles across the domain of definition of the flow. Despite its embarrassingly parallel nature, this computation creates a performance bottleneck in the analysis of large-scale datasets that existing adaptive techniques alleviate only partially. Our iterative approximation method significantly improves upon the state of the art by precisely modeling the flow behavior around automatically detected geometric structures embedded in the flow, thus effectively restricting the sampling effort to interesting regions. Our data reconstruction is based on a modified version of Sibson's scattered data interpolation and allows us at each step to offer an intermediate dense approximation of the flow map and to seamlessly integrate regions that will be further refined in subsequent steps. We present a quantitative and qualitative evaluation of our method on different types of flow datasets and offer a detailed comparison with existing techniques.