DevLan/vispubs huggingface dataset:
Visualizing Expanded Query Results
Authors: Michael Mazurek, Manuela Waldner
Published at EuroVis, 2018

When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search.
Visualizing Multidimensional Data with Order Statistics
Authors: Mukund Raj, Ross T. Whitaker
Published at EuroVis, 2018

Multidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.
Visualizing the Phase Space of Heterogeneous Inertial Particles in 2D Flows
Authors: Irene Baeza Rojo, Markus H. Gross, Tobias Günther
Published at EuroVis, 2018

In many scientific disciplines, the motion of finite‐sized objects in fluid flows plays an important role, such as in brownout engineering, sediment transport, oceanology or meteorology. These finite‐sized objects are called inertial particles and, in contrast to traditional tracer particles, their motion depends on their current position, their own particle velocity, the time and their size. Thus, the visualization of their motion becomes a high‐dimensional problem that entails computational and perceptual challenges. So far, no visualization explored and visualized the particle trajectories under variation of all seeding parameters. In this paper, we propose three coordinated views that visualize the different aspects of the high‐dimensional space in which the particles live. We visualize the evolution of particles over time, showing that particles travel different distances in the same time, depending on their size. The second view provides a clear illustration of the trajectories of different particle sizes and allows the user to easily identify differences due to particle size. Finally, we embed the trajectories in the space‐velocity domain and visualize their distance to an attracting manifold using ribbons. In all views, we support interactive linking and brushing, and provide abstraction through density volumes that are shown by direct volume rendering and isosurface slabs. Using our method, users gain deeper insights into the dynamics of inertial particles in 2D fluids, including size‐dependent separation, preferential clustering and attraction. We demonstrate the effectiveness of our method in multiple steady and unsteady 2D flows.
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration
Authors: Marco Cavallo, Çagatay Demiralp
Published at CHI, 2018

Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.
An Eye For Design: Gaze Visualizations for Remote Collaborative Work
Authors: Sarah D'Angelo, Darren Gergle
Published at CHI, 2018

In remote collaboration, gaze visualizations are designed to display where collaborators are looking in a shared visual space. This type of gaze-based intervention can improve coordination, however researchers have yet to fully explore different gaze visualization techniques and develop a deeper understanding of the ways in which features of visualizations may interact with task attributes to influence collaborative performance. There are many ways to visualize characteristics of eye movements, such as a path connecting fixation points or a heat map illustrating fixation duration and coverage. In this study, we designed and evaluated three unique gaze visualizations in a remote search task. Our results suggest that the design of gaze visualizations affects performance, coordination, searching behavior, and perceived utility. Additionally, the degree of task coupling further influences the effect of gaze visualizations on performance and coordination. We then reflect on the value of gaze visualizations for remote work and discuss implications for the design of gaze-based interventions.
Animated Edge Textures in Node-Link Diagrams: a Design Space and Initial Evaluation
Authors: Hugo Romat, Caroline Appert, Benjamin Bach, Nathalie Henry Riche, Emmanuel Pietriga
Published at CHI, 2018

Network 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.
Augmenting Code with In Situ Visualizations to Aid Program Understanding
Authors: Jane Hoffswell, Arvind Satyanarayan, Jeffrey Heer
Published at CHI, 2018

Programmers must draw explicit connections between their code and runtime state to properly assess the correctness of their programs. However, debugging tools often decouple the program state from the source code and require explicitly invoked views to bridge the rift between program editing and program understanding. To unobtrusively reveal runtime behavior during both normal execution and debugging, we contribute techniques for visualizing program variables directly within the source code. We describe a design space and placement criteria for embedded visualizations. We evaluate our in situ visualizations in an editor for the Vega visualization grammar. Compared to a baseline development environment, novice Vega users improve their overall task grade by about 2 points when using the in situ visualizations and exhibit significant positive effects on their self-reported speed and accuracy.
Beagle: Automated Extraction and Interpretation of Visualizations from the Web
Authors: Leilani Battle, Peitong Duan, Zachery Miranda, Dana Mukusheva, Remco Chang, Michael Stonebraker
Published at CHI, 2018

"How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 85% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare for the visualization tools that were studied. Our findings also suggest that the total visualization types supported by a given tool could factor into its ease of use. However this effect appears to be mitigated by providing a variety of diverse expert visualization examples to users.
c.light: A Tool for Exploring Light Properties in Early Design Stage
Authors: Kyeong-Ah Jeong, EunJin Kim, Taesu Kim, Hyeon-Jeong Suk
Published at CHI, 2018

Although a light becomes an important design element, there are little techniques available to explore shapes and light effects in early design stages. We present c.light, a design tool that consists of a set of modules and a mobile application for visualizing the light in a physical world. It allows designers to easily fabricate both tangible and intangible properties of a light without a technical barrier. We analyzed how c.light contributes to the ideation process of light design through a workshop. The results showed that c.light largely expands designers' capability to manipulate intangible properties of light and, by doing so, it facilitates collaborative and inverted ideation process in early design stages. It is expected that the results of this study could enhance our understanding of how designers manipulate light in a physical world in early design stages and could be a good stepping stone for future tool development.
Clusters, Trends, and Outliers: How Immersive Technologies Can Facilitate the Collaborative Analysis of Multidimensional Data
Authors: Simon Butscher, Sebastian Hubenschmid, Jens Müller 0001, Johannes Fuchs 0001, Harald Reiterer
Published at CHI, 2018

Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.
Considering Agency and Data Granularity in the Design of Visualization Tools
Authors: Gonzalo Gabriel Méndez, Miguel A. Nacenta, Uta Hinrichs
Published at CHI, 2018

Previous research has identified trade-offs when it comes to designing visualization tools. While constructive "bottom-up' tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.
CraftML: 3D Modeling is Web Programming
Authors: Tom Yeh, Jeeeun Kim
Published at CHI, 2018

We explore web programming as a new paradigm for programmatic 3D modeling. Most existing approaches subscribe to the imperative programming paradigm. While useful, there exists a gulf of evaluation between procedural steps and the intended structure. We present CraftML, a language providing a declarative syntax where the code is the structure. CraftML offers a rich set of programming features familiar to web developers of all skill levels, such as tags, hyperlinks, document object model, cascade style sheet, JQuery, string interpolation, template engine, data injection, and scalable vector graphics. We develop an online IDE to support CraftML development, with features such as live preview, search, module import, and parameterization. Using examples and case studies, we demonstrate that CraftML offers a low floor for beginners to make simple designs, a high ceiling for experts to build complex computational models, and wide walls to support many application domains such as education, data physicalization, tactile graphics, assistive devices, and mechanical components.
CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations
Authors: Divit P. Singh, Lee Lisle, T. M. Murali 0001, Kurt Luther
Published at CHI, 2018

Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.
Data Illustrator: Augmenting Vector Design Tools with Lazy Data Binding for Expressive Visualization Authoring
Authors: Zhicheng Liu 0001, John Thompson 0002, Alan Wilson 0004, Mira Dontcheva, James Delorey, Sam Grigg, Bernard Kerr, John T. Stasko
Published at CHI, 2018

Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers' workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations.
DataInk: Direct and Creative Data-Oriented Drawing
Authors: Haijun Xia, Nathalie Henry Riche, Fanny Chevalier, Bruno Rodrigues De Araújo, Daniel Wigdor
Published at CHI, 2018

Creating whimsical, personal data visualizations remains a challenge due to a lack of tools that enable for creative visual expression while providing support to bind graphical content to data. Many data analysis and visualization creation tools target the quick generation of visual representations, but lack the functionality necessary for graphics design. Toolkits and charting libraries offer more expressive power, but require expert programming skills to achieve custom designs. In contrast, sketching affords fluid experimentation with visual shapes and layouts in a free-form manner, but requires one to manually draw every single data point. We aim to bridge the gap between these extremes. We propose DataInk, a system supports the creation of expressive data visualizations with rigorous direct manipulation via direct pen and touch input. Leveraging our commonly held skills, coupled with a novel graphical user interface, DataInk enables direct, fluid, and flexible authoring of creative data visualizations.
Design Patterns for Data Comics
Authors: Benjamin Bach, Zezhong Wang 0001, Matteo Farinella, Dave Murray-Rust, Nathalie Henry Riche
Published at CHI, 2018

Data comics for data-driven storytelling are inspired by the visual language of comics and aim to communicate insights in data through visualizations. While comics are widely known, few examples of data comics exist and there has not been any structured analysis nor guidance for their creation. We introduce data-comic design-patterns, each describing a set of panels with a specific narrative purpose, that allow for rapid storyboarding of data comics while showcasing their expressive potential. Our patterns are derived from i) analyzing common patterns in infographics, datavideos, and existing data comics, ii) our experiences creating data comics for different scenarios. Our patterns demonstrate how data comics allow an author to combine the best of both worlds: spatial layout and overview from infographics as well as linearity and narration from videos and presentations.
Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets
Authors: Justin Matejka, Michael Glueck, Erin Bradner, Ali Hashemi 0001, Tovi Grossman, George W. Fitzmaurice
Published at CHI, 2018

This paper presents Dream Lens, an interactive visual analysis tool for exploring and visualizing large-scale generative design datasets. Unlike traditional computer aided design, where users create a single model, with generative design, users specify high-level goals and constraints, and the system automatically generates hundreds or thousands of candidates all meeting the design criteria. Once a large collection of design variations is created, the designer is left with the task of finding the design, or set of designs, which best meets their requirements. This is a complicated task which could require analyzing the structural characteristics and visual aesthetics of the designs. Two studies are conducted which demonstrate the usability and usefulness of the Dream Lens system, and a generatively designed dataset of 16,800 designs for a sample design problem is described and publicly released to encourage advancement in this area.
Experiential Augmentation: Uncovering The Meaning of Qualitative Visualizations when Applied to Augmented Objects
Authors: Dixon Lo, Dan Lockton, Stacie Rohrbach
Published at CHI, 2018

As we move toward commercial usage of ubiquitous computing and augmented reality, it is important to think about how computing should communicate with us when it is distributed in our environment. This paper proposes that qualitative indexical visualizations based on learned understanding of physical phenomena (Experiential Augmentation) can enhance our interaction design language and aid digital interfaces in communicating in a real-world context. We present a study that gathers data on how participants interpret such visualizations, and propose a model with which to analyze their responses. Finally, we also give a set of design recommendations for those interested in creating similar augmentations.
Exploration and Explanation in Computational Notebooks
Authors: Adam Rule, Aurélien Tabard, James D. Hollan
Published at CHI, 2018

Computational notebooks combine code, visualizations, and text in a single document. Researchers, data analysts, and even journalists are rapidly adopting this new medium. We present three studies of how they are using notebooks to document and share exploratory data analyses. In the first, we analyzed over 1 million computational notebooks on GitHub, finding that one in four had no explanatory text but consisted entirely of visualizations or code. In a second study, we examined over 200 academic computational notebooks, finding that although the vast majority described methods, only a minority discussed reasoning or results. In a third study, we interviewed 15 academic data analysts, finding that most considered computational notebooks personal, exploratory, and messy. Importantly, they typically used other media to share analyses. These studies demonstrate a tension between exploration and explanation in constructing and sharing computational notebooks. We conclude with opportunities to encourage explanation in computational media without hindering exploration.