DevLan/vispubs huggingface dataset:
Statsplorer: Guiding Novices in Statistical Analysis
Authors: Chat Wacharamanotham, Krishna Subramanian 0002, Sarah Theres Völkel, Jan O. Borchers
Published at CHI, 2015

Each step of statistical analysis requires researchers to make decisions based on both statistical knowledge and the knowledge of their own data. For novice analysts, this is cognitively demanding and can lead to mistakes and misinterpretations of the results. We present Statsplorer, a software that helps novices learn and perform inferential statistical tests. It lets the user kick-start data analysis from their research questions. Statsplorer automatically tests necessary statistical assumptions and uses visualizations to guide the user in both selecting statistical tests and interpreting the results. We compared Statsplorer with a statistics lecture and investigated how Statsplorer prepares novices for learning statistics in an AB/BA crossover experiment. The results indicates that using Statsplorer prior to the lecture leads to significantly better test scores in understanding statistical assumptions and choosing appropriate statistical tests. Statsplorer is open-source and is available online at: http://hci.rwth-aachen.de/statsplorer.
Stock Lamp: An Engagement-Versatile Visualization Design
Authors: Yuzuru Tanahashi, Kwan-Liu Ma
Published at CHI, 2015

Design methodologies for information visualizations are typically based on the assumption that the users will be fully engaged in the visual exploration of the displayed information. However, recent research suggests that there is an increasing diversity in how users engage with modern visualizations, and that the traditional design theories do not always satisfy the varied users needs. In this paper, we present a new design concept, engagement-versatile design, for visualizations that target users with a variety of engagement styles. Without losing generality, we demonstrate the feasibility of this concept through the designing of a system called Stock Lamp, an engagement-versatile visualization that helps users keep track of the stock market in real-time. This design process includes identifying different modes of engagement, deriving design implications from each engagement-mode, and applying them to the visualization's design. Our user study shows that Stock Lamp is able to consistently relay market information even when the users are multi-tasking. We believe this study establishes a new concept that promotes a systematic design approach that leverages both theoretical and empirical design methodologies for future visualization development.
Storytelling in Information Visualizations: Does it Engage Users to Explore Data?
Authors: Jeremy Boy, Françoise Détienne, Jean-Daniel Fekete
Published at CHI, 2015

We present the results of three web-based field experiments, in which we evaluate the impact of using initial narrative visualization techniques and storytelling on user-engagement with exploratory information visualizations. We conducted these experiments on a popular news and opinion outlet, and on a popular visualization gallery website. While data-journalism exposes visualizations to a large public, we do not know how effectively this public makes sense of interactive graphics, and in particular if people explore them to gain additional insight to that provided by the journalists. In contrast to our hypotheses, our results indicate that augmenting exploratory visualizations with introductory 'stories' does not seem to increase user-engagement in exploration.
STRATOS: Using Visualization to Support Decisions in Strategic Software Release Planning
Authors: Bon Adriel Aseniero, Tiffany Wun, David Ledo, Guenther Ruhe, Anthony Tang 0001, Sheelagh Carpendale
Published at CHI, 2015

Software is typically developed incrementally and released in stages. Planning these releases involves deciding which features of the system should be implemented for each release. This is a complex planning process involving numerous trade-offs-constraints and factors that often make decisions difficult. Since the success of a product depends on this plan, it is important to understand the trade-offs between different release plans in order to make an informed choice. We present STRATOS, a tool that simultaneously visualizes several software release plans. The visualization shows several attributes about each plan that are important to planners. Multiple plans are shown in a single layout to help planners find and understand the trade-offs between alternative plans. We evaluated our tool via a qualitative study and found that STRATOS enables a range of decision-making processes, helping participants decide on which plan is most optimal.
TastyBeats: Designing Palatable Representations of Physical Activity
Authors: Rohit Ashok Khot, Jeewon Lee, Deepti Aggarwal, Larissa Hjorth, Florian 'Floyd' Mueller
Published at CHI, 2015

In this paper, we introduce palatable representations that besides improving the understanding of physical activity through abstract visualization also provide an appetizing drink to celebrate the experience of being physically active. By designing such palatable representations, our aim is to offer novel opportunities for reflection on one's physical activities. We present TastyBeats, a fountain-based interactive system that creates a fluidic spectacle of mixing sport drinks based on heart rate data of physical activity, which the user can later consume to replenish the loss of body fluids due to the physical activity. We articulate our experiences in designing the system as well as learning gained through field deployments of the system in participants' homes for a period of two weeks. We found that our system increased participants' awareness of physical activity and facilitated a shared social experience, while the prepared drink was treated as a hedonic reward that motivated participants to exercise more. Ultimately, with this work, we aim to inspire and guide design thinking on palatable representations, which we believe opens up new interaction possibilities to support physical activity experience.
The Effects of Representation and Juxtaposition on Graphical Perception of Matrix Visualization
Authors: Xiaotong Liu, Han-Wei Shen
Published at CHI, 2015

Analyzing multiple networks at once is a common yet difficult task in many domains. Using adjacency matrices for this purpose, however, can be effective because of its superior ability to accommodate dense networks in a small area. We evaluate various representations and juxtaposition designs for visualizing adjacency matrices through a series of controlled experiments. We investigate the effect of using square matrices and triangular matrices on the speed and accuracy of performing graphical-perception tasks. Based on human symmetric perception, we propose two alternative juxtaposition designs to the conventional side-by-side juxtaposition, and study how users perform visual search and comparison tasks regarding different juxtaposition types. Our results show that the matrix representations have similar performance, and the matrix juxtaposition types perform differently. With the design guidelines derived from our studies, we present a compact visualization termed TileMatrix for juxtaposing a large number of matrices, and demonstrate its effectiveness in analyzing multi-faceted, time-varying networks using real-world data.
Trajectory Bundling for Animated Transitions
Authors: Fan Du, Nan Cao 0001, Jian Zhao 0010, Yu-Ru Lin
Published at CHI, 2015

Animated transition has been a popular design choice for smoothly switching between different visualization views or layouts, in which movement trajectories are created as cues for tracking objects during location shifting. Tracking moving objects, however, becomes difficult when their movement paths overlap or the number of tracking targets increases. We propose a novel design to facilitate tracking moving objects in animated transitions. Instead of simply animating an object along a straight line, we create "bundled" movement trajectories for a group of objects that have spatial proximity and share similar moving directions. To study the effect of bundled trajectories, we untangle variations due to different aspects of tracking complexity in a comprehensive controlled user study. The results indicate that using bundled trajectories is particularly effective when tracking more targets (six vs. three targets) or when the object movement involves a high degree of occlusion or deformation. Based on the study, we discuss the advantages and limitations of the new technique, as well as provide design implications.
TurkBench: Rendering the Market for Turkers
Authors: Benjamin V. Hanrahan, Jutta K. Willamowski, Saiganesh Swaminathan, David B. Martin 0001
Published at CHI, 2015

Crowdsourcing is a relatively new model of labor where both the workers and work providers are experiencing its growing pains. A dominant platform that implements this model of labor is Amazon Mechanical Turk (AMT). While AMT has evolved over the years, the changes have focused mainly on work providers and have not addressed the problems workers face (e.g. dealing with market volatility and unpaid time searching for work). In this paper we present emph{TurkBench}, a tool meant to provide workers with personalized market visualization and session management. We discuss the design philosophy of the tool, briefly discuss four Turkers' reaction to a demo, and outline future work.
Twist and Learn: Interface Learning in 3DOF Exploration of 3D Scatterplots
Authors: Mark M. Shovman, James L. Bown, Andrea Szymkowiak, Kenneth C. Scott-Brown
Published at CHI, 2015

The increasing availability of 3D interfaces brings promise of improved user experience in diverse areas. Our study focuses on visual analytics, testing whether 3D interactivity improves performance in a visual data exploration task. Specifically, we compared scene rotation around vertical axis to a full 3D rotation using a InterSense IS-900 3D controller, in a task involving trivariate trend detection in a 3D scatterplot. We found that, while 3D rotation leads to slower performance, previous exposure to single-axis rotation removes that difference. This shows that an interactive 3D scatterplot can be an effective visual exploration technique for detecting trivariate patterns in the data, and highlights the role of interface learning in design and assessment of novel interfaces.
Understanding Data Videos: Looking at Narrative Visualization through the Cinematography Lens
Authors: Fereshteh Amini, Nathalie Henry Riche, Bongshin Lee, Christophe Hurter, Pourang Irani
Published at CHI, 2015

Data videos, motion graphics that incorporate visualizations about facts, are increasingly gaining popularity as a means of telling stories with data. However, very little is systematically recorded about (a) what elements are featured in data videos and (b) the processes used to create them. In this article, we provide initial insights to build this knowledge. We first report on a qualitative analysis of 50 professionally designed data videos, extracting and exposing their most salient constituents. Second, we report on a series of workshops with experienced storytellers from cinematography, graphics design and screenplay writing. We provided them with a set of data facts and visualizations and observed them create storyboards for data videos. From these exploratory studies, we derive broader implications for the design of an authoring tool to enable a wide audience to create data videos. Our findings highlight the importance of providing a flexible tool supporting a non-linear creation process and allowing users to iteratively go back to different phases of the process.
VeilMe: An Interactive Visualization Tool for Privacy Configuration of Using Personality Traits
Authors: Yang Wang, Liang Gou, Anbang Xu, Michelle X. Zhou, Huahai Yang, Hernan Badenes
Published at CHI, 2015

With the recent advances in using data analytics to automatically infer one's personality traits from their social media data, users are facing a growing tension between the use of the technology to aid self development in workplace and the privacy concerns of such use. Given the richness of personality data that can be derived today and the varied sensitivity of revealing such data, it is a non-trivial task for users to configure their privacy settings for sharing and protecting their derived personality data. Here we present the design, development, and evaluation of an interactive visualization tool, VeilMe, which helps users configure the privacy settings for the use of their personality portraits derived from social media. Unlike other privacy configuration tools, our tool offers two distinct advantages. First, it presents a novel and intuitive visual interface that aids users in understanding and exploring their own personality traits derived from their social media data, and configuring their privacy preferences. Second, our tool helps users to jump start their privacy settings by suggesting initial sharing strategies based on a set of factors, including the users' personality and target audience. We have evaluated the use of our tool with 124 participants in an enterprise context. Our results show that VeilMe effectively supports various user privacy configuration tasks, and also suggest several design implications, including the approaches to personalized privacy configurations.
#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media
Authors: Jian Zhao 0010, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins 0001
Published at VAST, 2014

We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.
A Five-Level Design Framework for Bicluster Visualizations
Authors: Maoyuan Sun, Chris North 0001, Naren Ramakrishnan
Published at VAST, 2014

Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.
A System for Visual Analysis of Radio Signal Data
Authors: Tarik Crnovrsanin, Chris Muelder, Kwan-Liu Ma
Published at VAST, 2014

Analysis of radio transmissions is vital for military defense as it provides valuable information about enemy communication and infrastructure. One challenge to the data analysis task is that there are far too many signals for analysts to go through by hand. Even typical signal meta data (such as frequency band, duration, and geographic location) can be overwhelming. In this paper, we present a system for exploring and analyzing such radio signal meta-data. Our system incorporates several visual representations for signal data, designed for readability and ease of comparison, as well as novel algorithms for extracting and classifying consistent signal patterns. We demonstrate the effectiveness of our system using data collected from real missions with an airborne sensor platform.
A Visual Reasoning Approach for Data-driven Transport Assessment on Urban Roads
Authors: Fei Wang 0016, Wei Chen 0001, Feiran Wu, Ye Zhao 0003, Han Hong, Tianyu Gu, Long Wang, Ronghua Liang, Hujun Bao
Published at VAST, 2014

Transport assessment plays a vital role in urban planning and traffic control, which are influenced by multi-faceted traffic factors involving road infrastructure and traffic flow. Conventional solutions can hardly meet the requirements and expectations of domain experts. In this paper we present a data-driven solution by leveraging a visual analysis system to evaluate the real traffic situations based on taxi trajectory data. A sketch-based visual interface is designed to support dynamic query and visual reasoning of traffic situations within multiple coordinated views. In particular, we propose a novel road-based query model for analysts to interactively conduct evaluation tasks. This model is supported by a bi-directional hash structure, TripHash, which enables real-time responses to the data queries over a huge amount of trajectory data. Case studies with a real taxi GPS trajectory dataset (&amp;gt; 30GB) show that our system performs well for on-demand transport assessment and reasoning.
An Insight- and Task-based Methodology for Evaluating Spatiotemporal Visual Analytics
Authors: Steven R. Gomez, Hua Guo, Caroline Ziemkiewicz, David H. Laidlaw
Published at VAST, 2014

We present a method for evaluating visualizations using both tasks and exploration, and demonstrate this method in a study of spatiotemporal network designs for a visual analytics system. The method is well suited for studying visual analytics applications in which users perform both targeted data searches and analyses of broader patterns. In such applications, an effective visualization design is one that helps users complete tasks accurately and efficiently, and supports hypothesis generation during open-ended exploration. To evaluate both of these aims in a single study, we developed an approach called layered insight- and task-based evaluation (LITE) that interposes several prompts for observations about the data model between sequences of predefined search tasks. We demonstrate the evaluation method in a user study of four network visualizations for spatiotemporal data in a visual analytics application. Results include findings that might have been difficult to obtain in a single experiment using a different methodology. For example, with one dataset we studied, we found that on average participants were faster on search tasks using a force-directed layout than using our other designs; at the same time, participants found this design least helpful in understanding the data. Our contributions include a novel evaluation method that combines well-defined tasks with exploration and observation, an evaluation of network visualization designs for spatiotemporal visual analytics, and guidelines for using this evaluation method.
An Integrated Visual Analysis System for Fusing MR Spectroscopy and Multi-Modal Radiology Imaging
Authors: Miguel Nunes, Benjamin Rowland, Matthias Schlachter, Soléakhéna Ken, Kresimir Matkovic, Anne Laprie, Katja Bühler
Published at VAST, 2014

For cancers such as glioblastoma multiforme, there is an increasing interest in defining "biological target volumes" (BTV), high tumour-burden regions which may be targeted with dose boosts in radiotherapy. The definition of a BTV requires insight into tumour characteristics going beyond conventionally defined radiological abnormalities and anatomical features. Molecular and biochemical imaging techniques, like positron emission tomography, the use of Magnetic Resonance (MR) Imaging contrast agents or MR Spectroscopy deliver this information and support BTV delineation. MR Spectroscopy Imaging (MRSI) is the only non-invasive technique in this list. Studies with MRSI have shown that voxels with certain metabolic signatures are more susceptible to predict the site of relapse. Nevertheless, the discovery of complex relationships between a high number of different metabolites, anatomical, molecular and functional features is an ongoing topic of research - still lacking appropriate tools supporting a smooth workflow by providing data integration and fusion of MRSI data with other imaging modalities. We present a solution bridging this gap which gives fast and flexible access to all data at once. By integrating a customized visualization of the multi-modal and multi-variate image data with a highly flexible visual analytics (VA) framework, it is for the first time possible to interactively fuse, visualize and explore user defined metabolite relations derived from MRSI in combination with markers delivered by other imaging modalities. Real-world medical cases demonstrate the utility of our solution. By making MRSI data available both in a VA tool and in a multi-modal visualization renderer we can combine insights from each side to arrive at a superior BTV delineation. We also report feedback from domain experts indicating significant positive impact in how this work can improve the understanding of MRSI data and its integration into radiotherapy planning.
Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration
Authors: Sungahn Ko, Shehzad Afzal, Simon J. Walton, Yang Yang, Junghoon Chae, Abish Malik, Yun Jang, Min Chen 0001, David S. Ebert
Published at VAST, 2014

This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study.