DevLan/vispubs huggingface dataset:
DocuCompass: Effective exploration of document landscapes
Authors: Florian Heimerl, Markus John, Qi Han 0006, Steffen Koch 0001, Thomas Ertl
Published at VAST, 2016

The creation of interactive visualization to analyze text documents has gained an impressive momentum in recent years. This is not surprising in the light of massive and still increasing amounts of available digitized texts. Websites, social media, news wire, and digital libraries are just few examples of the diverse text sources whose visual analysis and exploration offers new opportunities to effectively mine and manage the information and knowledge hidden within them. A popular visualization method for large text collections is to represent each document by a glyph in 2D space. These landscapes can be the result of optimizing pairwise distances in 2D to represent document similarities, or they are provided directly as meta data, such as geo-locations. For well-defined information needs, suitable interaction methods are available for these spatializations. However, free exploration and navigation on a level of abstraction between a labeled document spatialization and reading single documents is largely unsupported. As a result, vital foraging steps for task-tailored actions, such as selecting subgroups of documents for detailed inspection, or subsequent sense-making steps are hampered. To fill in this gap, we propose DocuCompass, a focus+context approach based on the lens metaphor. It comprises multiple methods to characterize local groups of documents, and to efficiently guide exploration based on users' requirements. DocuCompass thus allows for effective interactive exploration of document landscapes without disrupting the mental map of users by changing the layout itself. We discuss the suitability of multiple navigation and characterization methods for different spatializations and texts. Finally, we provide insights generated through user feedback and discuss the effectiveness of our approach.
DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction
Authors: Yuanzhe Chen, Qing Chen 0001, Mingqian Zhao, Sebastien Boyer, Kalyan Veeramachaneni, Huamin Qu
Published at VAST, 2016

Aiming at massive participation and open access education, Massive Open Online Courses (MOOCs) have attracted millions of learners over the past few years. However, the high dropout rate of learners is considered to be one of the most crucial factors that may hinder the development of MOOCs. To tackle this problem, statistical models have been developed to predict dropout behavior based on learner activity logs. Although predictive models can foresee the dropout behavior, it is still difficult for users to understand the reasons behind the predicted results and further design interventions to prevent dropout. In addition, with a better understanding of dropout, researchers in the area of predictive modeling in turn can improve the models. In this paper, we introduce DropoutSeer, a visual analytics system which not only helps instructors and education experts understand the reasons for dropout, but also allows researchers to identify crucial features which can further improve the performance of the models. Both the heterogeneous data extracted from three different kinds of learner activity logs (i.e., clickstream, forum posts and assignment records) and the predicted results are visualized in the proposed system. Case studies and expert interviews have been conducted to demonstrate the usefulness and effectiveness of DropoutSeer.
EventAction: Visual analytics for temporal event sequence recommendation
Authors: Fan Du, Catherine Plaisant, Neil Spring, Ben Shneiderman
Published at VAST, 2016

Recommender systems are being widely used to assist people in making decisions, for example, recommending films to watch or books to buy. Despite its ubiquity, the problem of presenting the recommendations of temporal event sequences has not been studied. We propose EventAction, which to our knowledge, is the first attempt at a prescriptive analytics interface designed to present and explain recommendations of temporal event sequences. EventAction provides a visual analytics approach to (1) identify similar records, (2) explore potential outcomes, (3) review recommended temporal event sequences that might help achieve the users' goals, and (4) interactively assist users as they define a personalized action plan associated with a probability of success. Following the design study framework, we designed and deployed EventAction in the context of student advising and reported on the evaluation with a student review manager and three graduate students.
Familiarity Vs Trust: A Comparative Study of Domain Scientists' Trust in Visual Analytics and Conventional Analysis Methods
Authors: Aritra Dasgupta, Joon-Yong Lee, Ryan Wilson, Robert A. Lafrance, Nick Cramer, Kristin A. Cook, Samuel H. Payne
Published at VAST, 2016

Combining interactive visualization with automated analytical methods like statistics and data mining facilitates data-driven discovery. These visual analytic methods are beginning to be instantiated within mixed-initiative systems, where humans and machines collaboratively influence evidence-gathering and decision-making. But an open research question is that, when domain experts analyze their data, can they completely trust the outputs and operations on the machine-side? Visualization potentially leads to a transparent analysis process, but do domain experts always trust what they see? To address these questions, we present results from the design and evaluation of a mixed-initiative, visual analytics system for biologists, focusing on analyzing the relationships between familiarity of an analysis medium and domain experts' trust. We propose a trust-augmented design of the visual analytics system, that explicitly takes into account domain-specific tasks, conventions, and preferences. For evaluating the system, we present the results of a controlled user study with 34 biologists where we compare the variation of the level of trust across conventional and visual analytic mediums and explore the influence of familiarity and task complexity on trust. We find that despite being unfamiliar with a visual analytic medium, scientists seem to have an average level of trust that is comparable with the same in conventional analysis medium. In fact, for complex sense-making tasks, we find that the visual analytic system is able to inspire greater trust than other mediums. We summarize the implications of our findings with directions for future research on trustworthiness of visual analytic systems.
GazeDx: Interactive Visual Analytics Framework for Comparative Gaze Analysis with Volumetric Medical Images
Authors: Hyunjoo Song, Jeongjin Lee, Tae Jung Kim, Kyoung Ho Lee, Bo Hyoung Kim, Jinwook Seo
Published at VAST, 2016

We present an interactive visual analytics framework, GazeDx (abbr. of GazeDiagnosis), for the comparative analysis of gaze data from multiple readers examining volumetric images while integrating important contextual information with the gaze data. Gaze pattern comparison is essential to understanding how radiologists examine medical images, and to identifying factors influencing the examination. Most prior work depended upon comparisons with manually juxtaposed static images of gaze tracking results. Comparative gaze analysis with volumetric images is more challenging due to the additional cognitive load on 3D perception. A recent study proposed a visualization design based on direct volume rendering (DVR) for visualizing gaze patterns in volumetric images; however, effective and comprehensive gaze pattern comparison is still challenging due to a lack of interactive visualization tools for comparative gaze analysis. We take the challenge with GazeDx while integrating crucial contextual information such as pupil size and windowing into the analysis process for more in-depth and ecologically valid findings. Among the interactive visualization components in GazeDx, a context-embedded interactive scatterplot is especially designed to help users examine abstract gaze data in diverse contexts by embedding medical imaging representations well known to radiologists in it. We present the results from two case studies with two experienced radiologists, where they compared the gaze patterns of 14 radiologists reading two patients' volumetric CT images.
How ideas flow across multiple social groups
Authors: Xiting Wang, Shixia Liu, Yang Chen, Tai-Quan Peng, Jing Su, Jing Yang, Baining Guo
Published at VAST, 2016

Tracking how correlated ideas flow within and across multiple social groups facilitates the understanding of the transfer of information, opinions, and thoughts on social media. In this paper, we present IdeaFlow, a visual analytics system for analyzing the lead-lag changes within and across pre-defined social groups regarding a specific set of correlated ideas, each of which is described by a set of words. To model idea flows accurately, we develop a random-walk-based correlation model and integrate it with Bayesian conditional cointegration and a tensor-based technique. To convey complex lead-lag relationships over time, IdeaFlow combines the strengths of a bubble tree, a flow map, and a timeline. In particular, we develop a Voronoi-treemap-based bubble tree to help users get an overview of a set of ideas quickly. A correlated-clustering-based layout algorithm is used to simultaneously generate multiple flow maps with less ambiguity. We also introduce a focus+context timeline to explore huge amounts of temporal data at different levels of time granularity. Quantitative evaluation and case studies demonstrate the accuracy and effectiveness of IdeaFlow.
Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration
Authors: Michael Behrisch 0001, Benjamin Bach, Michael Blumenschein, Michael Delz, Laura von Rüden, Jean-Daniel Fekete, Tobias Schreck
Published at VAST, 2016

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.
Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots
Authors: Junpeng Wang, Xiaotong Liu, Han-Wei Shen, Guang Lin
Published at VAST, 2016

Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.
NameClarifier: A Visual Analytics System for Author Name Disambiguation
Authors: Qiaomu Shen, Tongshuang Wu, Haiyan Yang, Yanhong Wu, Huamin Qu, Weiwei Cui
Published at VAST, 2016

In this paper, we present a novel visual analytics system called NameClarifier to interactively disambiguate author names in publications by keeping humans in the loop. Specifically, NameClarifier quantifies and visualizes the similarities between ambiguous names and those that have been confirmed in digital libraries. The similarities are calculated using three key factors, namely, co-authorships, publication venues, and temporal information. Our system estimates all possible allocations, and then provides visual cues to users to help them validate every ambiguous case. By looping users in the disambiguation process, our system can achieve more reliable results than general data mining models for highly ambiguous cases. In addition, once an ambiguous case is resolved, the result is instantly added back to our system and serves as additional cues for all the remaining unidentified names. In this way, we open up the black box in traditional disambiguation processes, and help intuitively and comprehensively explain why the corresponding classifications should hold. We conducted two use cases and an expert review to demonstrate the effectiveness of NameClarifier.
Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths
Authors: Zhicheng Liu, Yang Wang, Mira Dontcheva, Matthew Hoffman 0001, Seth Walker, Alan Wilson 0004
Published at VAST, 2016

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.
PhenoStacks: Cross-Sectional Cohort Phenotype Comparison Visualizations
Authors: Michael Glueck, Alina Gvozdik, Fanny Chevalier, Azam Khan, Michael Brudno, Daniel J. Wigdor
Published at VAST, 2016

Cross-sectional phenotype studies are used by genetics researchers to better understand how phenotypes vary across patients with genetic diseases, both within and between cohorts. Analyses within cohorts identify patterns between phenotypes and patients (e.g., co-occurrence) and isolate special cases (e.g., potential outliers). Comparing the variation of phenotypes between two cohorts can help distinguish how different factors affect disease manifestation (e.g., causal genes, age of onset, etc.). PhenoStacks is a novel visual analytics tool that supports the exploration of phenotype variation within and between cross-sectional patient cohorts. By leveraging the semantic hierarchy of the Human Phenotype Ontology, phenotypes are presented in context, can be grouped and clustered, and are summarized via overviews to support the exploration of phenotype distributions. The design of PhenoStacks was motivated by formative interviews with genetics researchers: we distil high-level tasks, present an algorithm for simplifying ontology topologies for visualization, and report the results of a deployment evaluation with four expert genetics researchers. The results suggest that PhenoStacks can help identify phenotype patterns, investigate data quality issues, and inform data collection design.
PorosityAnalyzer: Visual Analysis and Evaluation of Segmentation Pipelines to Determine the Porosity in Fiber-Reinforced Polymers
Authors: Johannes Weissenböck, Artem Amirkhanov, M. Eduard Gröller, Johann Kastner, Christoph Heinzl
Published at VAST, 2016

In this paper we present PorosityAnalyzer, a novel tool for detailed evaluation and visual analysis of pore segmentation pipelines to determine the porosity in fiber-reinforced polymers (FRPs). The presented tool consists of two modules: the computation module and the analysis module. The computation module enables a convenient setup and execution of distributed off-line-computations on industrial 3D X-ray computed tomography datasets. It allows the user to assemble individual segmentation pipelines in the form of single pipeline steps, and to specify the parameter ranges as well as the sampling of the parameter-space of each pipeline segment. The result of a single segmentation run consists of the input parameters, the calculated 3D binary-segmentation mask, the resulting porosity value, and other derived results (e.g., segmentation pipeline run-time). The analysis module presents the data at different levels of detail by drill-down filtering in order to determine accurate and robust segmentation pipelines. Overview visualizations allow to initially compare and evaluate the segmentation pipelines. With a scatter plot matrix (SPLOM), the segmentation pipelines are examined in more detail based on their input and output parameters. Individual segmentation-pipeline runs are selected in the SPLOM and visually examined and compared in 2D slice views and 3D renderings by using aggregated segmentation masks and statistical contour renderings. PorosityAnalyzer has been thoroughly evaluated with the help of twelve domain experts. Two case studies demonstrate the applicability of our proposed concepts and visualization techniques, and show that our tool helps domain experts to gain new insights and improve their workflow efficiency.
SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories
Authors: Shamal Al-Dohuki, Yingyu Wu, Farah Kamw, Jing Yang 0001, Xin Li, Ye Zhao 0003, Xinyue Ye, Wei Chen 0001, Chao Ma 0023, Fei Wang 0016
Published at VAST, 2016

Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing tools typically require users to select and brush geospatial regions on a map when retrieving and exploring taxi trajectories and passenger trips. To answer seemingly simple questions such as “What were the taxi trips starting from Main Street and ending at Wall Street in the morning?” or “Where are the taxis arriving at the Art Museum at noon typically coming from?”, tedious and time consuming interactions are usually needed since the numeric GPS points of trajectories are not directly linked to the keywords such as “Main Street”, “Wall Street”, and “Art Museum”. In this paper, we present SemanticTraj, a new method for managing and visualizing taxi trajectory data in an intuitive, semantic rich, and efficient means. With SemanticTraj, domain and public users can find answers to the aforementioned questions easily through direct queries based on the terms. They can also interactively explore the retrieved data in visualizations enhanced by semantic information of the trajectories and trips. In particular, taxi trajectories are converted into taxi documents through a textualization transformation process. This process maps GPS points into a series of street/POI names and pick-up/drop-off locations. It also converts vehicle speeds into user-defined descriptive terms. Then, a corpus of taxi documents is formed and indexed to enable flexible semantic queries over a text search engine. Semantic labels and meta-summaries of the results are integrated with a set of visualizations in a SemanticTraj prototype, which helps users study taxi trajectories quickly and easily. A set of usage scenarios are presented to show the usability of the system. We also collected feedback from domain experts and conducted a preliminary user study to evaluate the visual system.
SenseMap: Supporting browser-based online sensemaking through analytic provenance
Authors: Phong Hai Nguyen, Kai Xu 0003, Andy Bardill, Betul Salman, Kate Herd, B. L. William Wong
Published at VAST, 2016

Sensemaking is described as the process in which people collect, organize and create representations of information, all centered around some problem they need to understand. People often get lost when solving complicated tasks using big datasets over long periods of exploration and analysis. They may forget what they have done, are unaware of where they are in the context of the overall task, and are unsure where to continue. In this paper, we introduce a tool, SenseMap, to address these issues in the context of browser-based online sensemaking. We conducted a semi-structured interview with nine participants to explore their behaviors in online sensemaking with existing browser functionality. A simplified sensemaking model based on Pirolli and Card's model is derived to better represent the behaviors we found: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others. SenseMap automatically captures provenance of user sensemaking actions and provides multi-linked views to visualize the collected information and enable users to curate and communicate their findings. To explore how SenseMap is used, we conducted a user study in a naturalistic work setting with five participants completing the same sensemaking task related to their daily work activities. All participants found the visual representation and interaction of the tool intuitive to use. Three of them engaged with the tool and produced successful outcomes. It helped them to organize information sources, to quickly find and navigate to the sources they wanted, and to effectively communicate their findings.