38-Issue 3

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https://diglib.eg.org/handle/10.2312/2632780

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Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Krueger, Robert; Han, Qi; Ivanov, Nikolay; Mahtal, Sanae; Thom, Dennis; Pfister, Hanspeter; Ertl, Thomas; Gleicher, Michael and Viola, Ivan and Leitte, Heike
The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large-scale and up-to-date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up-to-date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal-and-task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back-end and an interactive visual analytics interface. The monitoring component uses intelligent pre-aggregation to allow dynamic queries in near real-time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.
Optimizing Stepwise Animation in Dynamic Set Diagrams
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Mizuno, Kazuyo; WU, Hsiang-Yun; Takahashi, Shigeo; Igarashi, Takeo; Gleicher, Michael and Viola, Ivan and Leitte, Heike
A set diagram represents the membership relation among data elements. It is often visualized as secondary information on top of primary information, such as the spatial positions of elements on maps and charts. Visualizing the temporal evolution of such set diagrams as well as their primary features is quite important; however, conventional approaches have only focused on the temporal behavior of the primary features and do not provide an effective means to highlight notable transitions within the set relationships. This paper presents an approach for generating a stepwise animation between set diagrams by decomposing the entire transition into atomic changes associated with individual data elements. The key idea behind our approach is to optimize the ordering of the atomic changes such that the synthesized animation minimizes unwanted set occlusions by considering their depth ordering and reduces the gaze shift between two consecutive stepwise changes. Experimental results and a user study demonstrate that the proposed approach effectively facilitates the visual identification of the detailed transitions inherent in dynamic set diagrams.
A Stable Graph Layout Algorithm for Processes
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Mennens, Robin; Scheepens, Roeland; Westenberg, Michel; Gleicher, Michael and Viola, Ivan and Leitte, Heike
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.
An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Chen, Min; Ebert, David; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the costbenefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect ''symptoms'', ''causes'', ''remedies'', and ''side-effects'', and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time-efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial-and-error in the lifecycle of VA system development.
DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kakar, Tabassum; Qin, Xiao; Rundensteiner, Elke A.; Harrison, Lane; Sahoo, Sanjay K.; De, Suranjan; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Adverse reactions caused by drug-drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post-marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DIVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain-specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in-depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case-studies and interviews by drug analysts at the US Food and Drug Administration - both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug-drug interactions.
Focus+Context Exploration of Hierarchical Embeddings
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Höllt, Thomas; Vilanova, Anna; Pezzotti, Nicola; Lelieveldt, Boudewijn P. F.; Hauser, Helwig; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level-of-detail-hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi-dimensional images.
Oui! Outlier Interpretation on Multi-dimensional Data via Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhao, Xun; Cui, Weiwei; Wu, Yanhong; Zhang, Haidong; Qu, Huamin; Zhang, Dongmei; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
Designing Animated Transitions to Convey Aggregate Operations
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Younghoon; Correll, Michael; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Data can be aggregated in many ways before being visualized in charts, profoundly affecting what a chart conveys. Despite this importance, the type of aggregation is often communicated only via axis titles. In this paper, we investigate the use of animation to disambiguate different types of aggregation and communicate the meaning of aggregate operations. We present design rationales for animated transitions depicting aggregate operations and present the results of an experiment assessing the impact of these different transitions on identification tasks. We find that judiciously staged animated transitions can improve subjects' accuracy at identifying the aggregation performed, though sometimes with longer response times than with static transitions. Through an analysis of participants' rankings and qualitative responses, we find a consistent preference for animation over static transitions and highlight visual features subjects report relying on to make their judgments. We conclude by extending our animation designs to more complex charts of aggregated data such as box plots and bootstrapped confidence intervals.
An Exploratory User Study of Visual Causality Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Yen, Chi-Hsien Eric; Parameswaran, Aditya; Fu, Wai-Tat; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.
Visualization of Equivalence in 2D Bivariate Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Zheng, Boyan; Rieck, Bastian; Leitte, Heike; Sadlo, Filip; Gleicher, Michael and Viola, Ivan and Leitte, Heike
In this paper, we show how the equivalence property leads to the novel concept of equivalent regions in mappings from Rn to Rn. We present a technique for obtaining these regions both in the domain and the codomain of such a mapping, and determine their correspondence. This enables effective investigation of variation equivalence within mappings, and between mappings in terms of comparative visualization. We implement our approach for n = 2, and demonstrate its utility using different examples.
CV3: Visual Exploration, Assessment, and Comparison of CVs
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Filipov, Velitchko; Arleo, Alessio; Federico, Paolo; Miksch, Silvia; Gleicher, Michael and Viola, Ivan and Leitte, Heike
The Curriculum Vitae (CV, also referred to as ''résumé'') is an established representation of a person's academic and professional history. A typical CV is comprised of multiple sections associated with spatio-temporal, nominal, hierarchical, and ordinal data. The main task of a recruiter is, given a job application with specific requirements, to compare and assess CVs in order to build a short list of promising candidates to interview. Commonly, this is done by viewing CVs in a side-by-side fashion. This becomes challenging when comparing more than two CVs, because the reader is required to switch attention between them. Furthermore, there is no guarantee that the CVs are structured similarly, thus making the overview cluttered and significantly slowing down the comparison process. In order to address these challenges, in this paper we propose ''CV3'', an interactive exploration environment offering users a new way to explore, assess, and compare multiple CVs, to suggest suitable candidates for specific job requirements. We validate our system by means of domain expert feedback whose results highlight both the efficacy of our approach and its limitations. We learned that CV3 eases the overall burden of recruiters thereby assisting them in the selection process.
Latent Space Cartography: Visual Analysis of Vector Space Embeddings
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Liu, Yang; Jun, Eunice; Li, Qisheng; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Latent spaces-reduced-dimensionality vector space embeddings of data, fit via machine learning-have been shown to capture interesting semantic properties and support data analysis and synthesis within a domain. Interpretation of latent spaces is challenging because prior knowledge, sometimes subtle and implicit, is essential to the process. We contribute methods for ''latent space cartography'', the process of mapping and comparing meaningful semantic dimensions within latent spaces. We first perform a literature survey of relevant machine learning, natural language processing, and scientific research to distill common tasks and propose a workflow process. Next, we present an integrated visual analysis system for supporting this workflow, enabling users to discover, define, and verify meaningful relationships among data points, encoded within latent space dimensions. Three case studies demonstrate how users of our system can compare latent space variants in image generation, challenge existing findings on cancer transcriptomes, and assess a word embedding benchmark.
Investigating Effects of Visual Anchors on Decision-Making about Misinformation
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Wesslen, Ryan; Santhanam, Sashank; Karduni, Alireza; Cho, Isaac; Shaikh, Samira; Dou, Wenwen; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Cognitive biases are systematic errors in judgment due to an over-reliance on rule-of-thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision-making. To investigate, we performed a between-subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision-making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.
netflower: Dynamic Network Visualization for Data Journalists
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Stoiber, Christina; Rind, Alexander; Grassinger, Florian; Gutounig, Robert; Goldgruber, Eva; Sedlmair, Michael; Emrich, Štefan; Aigner, Wolfgang; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four-year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story-finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/
A Framework for GPU-accelerated Exploration of Massive Time-varying Rectilinear Scalar Volumes
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Marton, Fabio; Agus, Marco; Gobbetti, Enrico; Gleicher, Michael and Viola, Ivan and Leitte, Heike
We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out-of-core representation, based on per-frame levels of hierarchically tiled non-redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low-bitrate codec able to store into fixed-size pages a variable-rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time-critical operations, while a near-lossless representation is employed to support high-quality static frame rendering. A flexible high-speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object-space and image-space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high-quality snapshots generated from near-lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi-billion-voxel frames.
Interactive Visualization of Flood and Heavy Rain Simulations
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Cornel, Daniel; Buttinger-Kreuzhuber, Andreas; Konev, Artem; Horváth, Zsolt; Wimmer, Michael; Heidrich, Raimund; Waser, Jürgen; Gleicher, Michael and Viola, Ivan and Leitte, Heike
In this paper, we present a real-time technique to visualize large-scale adaptive height fields with C1-continuous surface reconstruction. Grid-based shallow water simulation is an indispensable tool for interactive flood management applications. Height fields defined on adaptive grids are often the only viable option to store and process the massive simulation data. Their visualization requires the reconstruction of a continuous surface from the spatially discrete simulation data. For regular grids, fast linear and cubic interpolation are commonly used for surface reconstruction. For adaptive grids, however, there exists no higher-order interpolation technique fast enough for interactive applications. Our proposed technique bridges the gap between fast linear and expensive higher-order interpolation for adaptive surface reconstruction. During reconstruction, no matter if regular or adaptive, discretization and interpolation artifacts can occur, which domain experts consider misleading and unaesthetic. We take into account boundary conditions to eliminate these artifacts, which include water climbing uphill, diving towards walls, and leaking through thin objects. We apply realistic water shading with visual cues for depth perception and add waves and foam synthesized from the simulation data to emphasize flow directions. The versatility and performance of our technique are demonstrated in various real-world scenarios. A survey conducted with domain experts of different backgrounds and concerned citizens proves the usefulness and effectiveness of our technique.
Ray Tracing Generalized Tube Primitives: Method and Applications
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Han, Mengjiao; Wald, Ingo; Usher, Will; Wu, Qi; Wang, Feng; Pascucci, Valerio; Hansen, Charles D.; Johnson, Chris R.; Gleicher, Michael and Viola, Ivan and Leitte, Heike
We present a general high-performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed- and varying-radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high-quality rendering, with low memory overhead.
Kyrix: Interactive Pan/Zoom Visualizations at Scale
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Tao, Wenbo; Liu, Xiaoyu; Wang, Yedi; Battle, Leilani; Demiralp, Çagatay; Chang, Remco; Stonebraker, Michael; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Pan and zoom are basic yet powerful interaction techniques for exploring large datasets. However, existing zoomable UI toolkits such as Pad++ and ZVTM do not provide the backend database support and data-driven primitives that are necessary for creating large-scale visualizations. This limitation in existing general-purpose toolkits has led to many purpose-built solutions (e.g. Google Maps and ForeCache) that address the issue of scalability but cannot be easily extended to support visualizations beyond their intended data types and usage scenarios. In this paper, we introduce Kyrix to ease the process of creating general and large-scale web-based pan/zoom visualizations. Kyrix is an integrated system that provides the developer with a concise and expressive declarative language along with a backend support for performance optimization of large-scale data. To evaluate the scalability of Kyrix, we conducted a set of benchmarked experiments and show that Kyrix can support high interactivity (with an average latency of 100 ms or below) on pan/zoom visualizations of 100 million data points. We further demonstrate the accessibility of Kyrix through an observational study with 8 developers. Results indicate that developers can quickly learn Kyrix's underlying declarative model to create scalable pan/zoom visualizations. Finally, we provide a gallery of visualizations and show that Kyrix is expressive and flexible in that it can support the developer in creating a wide range of customized visualizations across different application domains and data types.
Topic Tomographies (TopTom): a Visual Approach to Distill Information From Media Streams
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Gobbo, Beatrice; Balsamo, Duilio; Mauri, Michele; Bajardi, Paolo; Panisson, André; CIUCCARELLI, PAOLO; Gleicher, Michael and Viola, Ivan and Leitte, Heike
In this paper we present TopTom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user-generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low-dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. TopTom implements a batch processing pipeline able to run both in near-real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast-like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.
Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Battle, Leilani; Heer, Jeffrey; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open-ended and more focused exploration tasks.

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