DevLan/vispubs huggingface dataset:
Sample-oriented task-driven visualizations: allowing users to make better, more confident decisions
Authors: Nivan Ferreira, Danyel Fisher, Arnd Christian König
Published at CHI, 2014

We often use datasets that reflect samples, but many visualization tools treat data as full populations. Uncertain visualizations are good at representing data distributions emerging from samples, but are more limited in allowing users to carry out decision tasks. This is because tasks that are simple on a traditional chart (e.g. "compare two bars") become a complex probabilistic task on a chart with uncertainty. We present guidelines for creating visual annotations for solving tasks with uncertainty, and an implementation that addresses five core tasks on a bar chart. A preliminary user study shows promising results: that users have a justified confidence in their answers with our system.
Show me the invisible: visualizing hidden content
Authors: Thomas Geymayer, Markus Steinberger, Alexander Lex, Marc Streit, Dieter Schmalstieg
Published at CHI, 2014

Content on computer screens is often inaccessible to users because it is hidden, e.g., occluded by other windows, outside the viewport, or overlooked. In search tasks, the efficient retrieval of sought content is important. Current software, however, only provides limited support to visualize hidden occurrences and rarely supports search synchronization crossing application boundaries. To remedy this situation, we introduce two novel visualization methods to guide users to hidden content. Our first method generates awareness for occluded or out-of-viewport content using see-through visualization. For content that is either outside the screen's viewport or for data sources not opened at all, our second method shows off-screen indicators and an on-demand smart preview. To reduce the chances of overlooking content, we use visual links, i.e., visible edges, to connect the visible content or the visible representations of the hidden content. We show the validity of our methods in a user study, which demonstrates that our technique enables a faster localization of hidden content compared to traditional search functionality and thereby assists users in information retrieval tasks.
Smarties: an input system for wall display development
Authors: Olivier Chapuis, Anastasia Bezerianos, Stelios Frantzeskakis
Published at CHI, 2014

Wall-sized displays can support data visualization and collaboration, but making them interactive is challenging. Smarties allows wall application developers to easily add interactive support to their collaborative applications. It consists of an interface running on touch mobile devices for input, a communication protocol between devices and the wall, and a library that implements the protocol and handles synchronization, locking and input conflicts. The library presents the input as an event loop with callback functions. Each touch mobile has multiple cursor controllers, each associated with keyboards, widgets and clipboards. These controllers can be assigned to specific tasks, are persistent in nature, and can be shared by multiple collaborating users for sharing work. They can control simple cursors on the wall application, or specific content (objects or groups of them). The types of associated widgets are decided by the wall application, making the mobile interface customizable by the wall application it connects to.
SonicExplorer: fluid exploration of audio parameters
Authors: Alexander Travis Adams, Berto Gonzalez, Celine Latulipe
Published at CHI, 2014

In digital music production, the phrase "in the box" refers to the increasing replacement of extraneous hardware devices with compatible software components. As controls move from hard to soft, we have seen an increase in usability issues for musicians and sound engineers dealing with a large number of temporal inputs and both continuous and discrete controls. We present the SonicExplorer application, which we developed to give users a new interface for exploring and manipulating audio. SonicExplorer leverages users' spatial and color perception to enhance exploration by visualizing the parameter space and providing implicit memory cues. The application also leverages bimanual input to aid in fluid exploration of multidimensional audio parameter spaces, and to minimize the need for switching between parameters.
Structuring the space: a study on enriching node-link diagrams with visual references
Authors: Basak Alper, Nathalie Henry Riche, Tobias Höllerer
Published at CHI, 2014

Exploring large visualizations that do not fit in the screen raises orientation and navigation challenges. Structuring the space with additional visual references such as grids or contour lines provide spatial landmarks that may help viewers form a mental model of the space. However, previous studies report mixed results regarding their utility. While some evidence showed that grid and other visual embellishments improve memorability, experiments with contour lines suggest otherwise. In this work, we describe an evaluation framework to capture the impact of introducing visual references in node-link diagrams. We present the results of three controlled experiments that deepen our understanding on enriching large visualization spaces with visual structures. In particular, we provide the first tangible evidence that contour lines have significant benefits when navigating large node-link diagrams.
Supporting learners in collecting and exploring data from immersive simulations in collective inquiry
Authors: Michelle Lui, Alex C. Kuhn, Alisa Acosta, Chris Quintana, James D. Slotta
Published at CHI, 2014

Digitally augmented physical spaces (e.g., smart classrooms) offer opportunities to engage students in novel and potentially transformative learning experiences. This paper presents an immersive rainforest simulation and collective inquiry activity where students collect observational data from the environment and explore their peers' data through large visualization displays and personal mobile devices. Two iterations of the design were tested, which resulted in higher quality student explanations constructed. Images were found to be an important source of evidence for the explanations, more so than text-only evidence. We also found that patterns of collective ideas influenced student performance, and that visualizations, as ambient or plenary displays, supported both teacher and students in reviewing patterns of collected data.
Supporting the design and fabrication of physical visualizations
Authors: Saiganesh Swaminathan, Conglei Shi, Yvonne Jansen, Pierre Dragicevic, Lora Oehlberg, Jean-Daniel Fekete
Published at CHI, 2014

Physical visualizations come in increasingly diverse forms, and are used in domains including art and entertainment, business analytics, and scientific research. However, creating physical visualizations requires laborious craftsmanship and demands expertise in both data visualization and digital fabrication. We present three case studies that illustrate limitations of current visualization fabrication workflows. We then present MakerVis, a prototype tool that integrates the entire process of creating physical visualizations, from data filtering to physical fabrication. Design sessions with three end users demonstrate how tools such as MakerVis can dramatically lower the barriers to producing physical visualizations. Observations and interviews from these sessions highlighted future research areas, including customization support, using material properties to represent data variables, and allowing the reuse of physical data objects in new visualizations.
Task-driven evaluation of aggregation in time series visualization
Authors: Danielle Albers, Michael Correll, Michael Gleicher
Published at CHI, 2014

Many visualization tasks require the viewer to make judgments about aggregate properties of data. Recent work has shown that viewers can perform such tasks effectively, for example to efficiently compare the maximums or means over ranges of data. However, this work also shows that such effectiveness depends on the designs of the displays. In this paper, we explore this relationship between aggregation task and visualization design to provide guidance on matching tasks with designs. We combine prior results from perceptual science and graphical perception to suggest a set of design variables that influence performance on various aggregate comparison tasks. We describe how choices in these variables can lead to designs that are matched to particular tasks. We use these variables to assess a set of eight different designs, predicting how they will support a set of six aggregate time series comparison tasks. A crowd-sourced evaluation confirms these predictions. These results not only provide evidence for how the specific visualizations support various tasks, but also suggest using the identified design variables as a tool for designing visualizations well suited for various types of tasks.
The role of interactive biclusters in sensemaking
Authors: Maoyuan Sun, Lauren Bradel, Christopher L. North, Naren Ramakrishnan
Published at CHI, 2014

Visual exploration of relationships within large, textual datasets is an important aid for human sensemaking. By understanding computed, structural relationships between entities of different types (e.g., people and locations), users can leverage domain expertise and intuition to determine the importance and relevance of these relationships for tasks, such as intelligence analysis. Biclusters are a potentially desirable method to facilitate this, because they reveal coordinated relationships that can represent meaningful relationships. Bixplorer, a visual analytics prototype, supports interactive exploration of textual datasets in a spatial workspace with biclusters. In this paper, we present results of a study that analyzes how users interact with biclusters to solve an intelligence analysis problem using Bixplorer. We found that biclusters played four principal roles in the analytical process: an effective starting point for analysis, a revealer of two levels of connections, an indicator of potentially important entities, and a useful label for clusters of organized information.
Traffigram: distortion for clarification via isochronal cartography
Authors: Sungsoo (Ray) Hong, Yea-Seul Kim, Jong-Chul Yoon, Cecilia R. Aragon
Published at CHI, 2014

Most geographic maps visually represent physical distance; however, travel time can in some cases be more important than distance because it directly indicates availability. The technique of creating maps from temporal data is known as isochronal cartography, and is a form of distortion for clarification. In an isochronal map, congestion expands areas, while ideal travel conditions make the map shrink in comparison to the actual distance scale of a traditional map. Although there have been many applications of this technique, detailed user studies of its efficacy remain scarce, and there are conflicting views on its practical value. To attempt to settle this issue, we utilized a user-centered design process to determine which features of isochronal cartography might be most usable in practice. We developed an interactive cartographic visualization system, Traffigram, that features a novel combination of efficient isochronal map algorithms and an interface designed to give map users a quick and seamless experience while preserving geospatial integrity and aesthetics. We validated our design choices with multiple usability studies. We present our results and discuss implications for design.
Visualization of personal history for video navigation
Authors: Abir Al Hajri, Gregor Miller, Matthew Fong, Sidney S. Fels
Published at CHI, 2014

We present an investigation of two different visualizations of video history: Video Timeline and Video Tiles. Video Timeline extends the commonly employed list-based visualization for navigation history by applying size to indicate heuristics and occupying the full screen with a two-sided timeline. Video Tiles visualizes history items in a grid-based layout by following pre-defined templates based on items' heuristics and ordering, utilizing screen space more effectively at the expense of a clearer temporal location. The visualizations are compared against the state-of-the-art method (a filmstrip-based visualization), with ten participants tasked with sharing their previously-seen affective intervals. Our study shows that our visualizations are perceived as intuitive and both outperform and are strongly preferred to the current method. Based on these results, Video Timeline and Video Tiles provide an effective addition to video viewers to help manage the growing quantity of video. They provide users with insight into their navigation patterns, allowing them to quickly find previously-seen intervals, leading to efficient clip sharing, simpler authoring and video summarization.
Visualizing dynamic networks with matrix cubes
Authors: Benjamin Bach, Emmanuel Pietriga, Jean-Daniel Fekete
Published at CHI, 2014

Designing visualizations of dynamic networks is challenging, both because the data sets tend to be complex and because the tasks associated with them are often cognitively demand- ing. We introduce the Matrix Cube, a novel visual representation and navigation model for dynamic networks, inspired by the way people comprehend and manipulate physical cubes. Users can change their perspective on the data by rotating or decomposing the 3D cube. These manipulations can produce a range of different 2D visualizations that emphasize specific aspects of the dynamic network suited to particular analysis tasks. We describe Matrix Cubes and the interactions that can be performed on them in the Cubix system. We then show how two domain experts, an astronomer and a neurologist, used Cubix to explore and report on their own network data.
Visualizing interactive narratives: employing a branching comic to tell a story and show its readings
Authors: Daniel Andrews, Chris Baber
Published at CHI, 2014

This paper describes the design and evaluation of a branching comic to compare how readers recall a visual narrative when presented as an interactive, digital program, or as a linear sequence on paper. The layout of the comic is used to visualize this data as heat maps and explore patterns of users' recollections. We describe the theoretical justification for this based upon previous work in narrative visualizations, interactive stories and comics. Having tested the comic with school boys aged 11-12; we saw patterns in the data that complement other research in both interactive stories and visualizations. We argue that the heat maps helped identify these patterns, which have implications for future designs and analyses of interactive visual and/or narrative media.
A Partition-Based Framework for Building and Validating Regression Models
Authors: Thomas Mühlbacher, Harald Piringer
Published at VAST, 2013

Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models.
An Extensible Framework for Provenance in Human Terrain Visual Analytics
Authors: Rick Walker, Aidan Slingsby, Jason Dykes, Kai Xu 0003, Jo Wood, Phong Hai Nguyen, Derek Stephens, B. L. William Wong, Yongjun Zheng
Published at VAST, 2013

We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.
Decision Exploration Lab: A Visual Analytics Solution for Decision Management
Authors: Bertjan Broeksema, Thomas Baudel, Arthur G. Telea, Paolo Crisafulli
Published at VAST, 2013

We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.
Explainers: Expert Explorations with Crafted Projections
Authors: Michael Gleicher
Published at VAST, 2013

This paper introduces an approach to exploration and discovery in high-dimensional data that incorporates a user's knowledge and questions to craft sets of projection functions meaningful to them. Unlike most prior work that defines projections based on their statistical properties, our approach creates projection functions that align with user-specified annotations. Therefore, the resulting derived dimensions represent concepts defined by the user's examples. These especially crafted projection functions, or explainers, can help find and explain relationships between the data variables and user-designated concepts. They can organize the data according to these concepts. Sets of explainers can provide multiple perspectives on the data. Our approach considers tradeoffs in choosing these projection functions, including their simplicity, expressive power, alignment with prior knowledge, and diversity. We provide techniques for creating collections of explainers. The methods, based on machine learning optimization frameworks, allow exploring the tradeoffs. We demonstrate our approach on model problems and applications in text analysis.
HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies
Authors: Wenwen Dou, Li Yu, Derek Xiaoyu Wang, Zhiqiang Ma 0004, William Ribarsky
Published at VAST, 2013

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system - HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.