Multiple View Visualization Research Articles

Dowsing: a task-driven approach for multiple-view visualizations dynamic recommendation

Dowsing: a task-driven approach for multiple-view visualizations dynamic recommendation

Semi-Automatic Layout Adaptation for Responsive Multiple-View Visualization Design.

Multiple-view (MV) visualizations have become ubiquitous for visual communication and exploratory data visualization. However, most existing MV visualizations are designed for the desktop, which can be unsuitable for the continuously evolving displays of varying screen sizes. In this article, we present a two-stage adaptation framework that supports the automated retargeting and semi-automated tailoring of a desktop MV visualization for rendering on devices with displays of varying sizes. First, we cast layout retargeting as an optimization problem and propose a simulated annealing technique that can automatically preserve the layout of multiple views. Second, we enable fine-tuning for the visual appearance of each view, using a rule-based auto configuration method complemented with an interactive interface for chart-oriented encoding adjustment. To demonstrate the feasibility and expressivity of our proposed approach, we present a gallery of MV visualizations that have been adapted from the desktop to small displays. We also report the result of a user study comparing visualizations generated using our approach with those by existing methods. The outcome indicates that the participants generally prefer visualizations generated using our approach and find them to be easier to use.

MULTI-NETVIS: Visual Analytics for Multivariate Network

In the process of multivariate network exploration and analysis, it is important to consider network topology and attribute correlation analysis. In order to consider both in presentation and analysis, existing research focuses on visual design and multiple-view visualization. However, this multitudinous visual design makes network cognition and analysis difficult. In multi-view visualization, the associated information among attributes is rarely retained and is often accompanied by tedious interaction processes. In this paper, a layout scheme is proposed to balance attribute and topology analysis in multivariate network visual analysis and a multivariate network visual analytics system is implemented based on the layout scheme. The analysis scenarios of overall, community, and local multi-granularity are provided by the layout scheme, which combines 3D, 2.5D, and 2D layouts. According to the layout scheme, we propose a layout transformation method to maintain the relative position of the topological context layouts in three dimensions. Furthermore, we propose a Louvain-3D FDA layout algorithm for the 3D layout, and introduce an edge bundling algorithm in the 2.5D layout to achieve an attribute-oriented topology layout. Combining the principle of interaction from global to detail, we design a novel system, Multi-NetVis, which supports users in drilling exploration and analysis and takes both the network attribute correlations and topological structure into consideration. Finally, two datasets are selected to demonstrate the usage scenarios and an evaluation experiment is designed to verify the effectiveness of the layout scheme.

SightBi: Exploring Cross-View Data Relationships with Biclusters.

Multiple-view visualization (MV) has been heavily used in visual analysis tools for sensemaking of data in various domains (e.g., bioinformatics, cybersecurity and text analytics). One common task of visual analysis with multiple views is to relate data across different views. For example, to identify threats, an intelligence analyst needs to link people from a social network graph with locations on a crime-map, and then search for and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking), which may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, and then observing and following elements highlighted in other views. To address this, we present SightBi, a visual analytics approach for supporting cross-view data relationship explorations. We discuss the design rationale of SightBi in detail, with identified user tasks regarding the use of cross-view data relationships. SightBi formalizes cross-view data relationships as biclusters, computes them from a dataset, and uses a bi-context design that highlights creating stand-alone relationship-views. This helps preserve existing views and offers an overview of cross-view data relationships to guide user exploration. Moreover, SightBi allows users to interactively manage the layout of multiple views by using newly created relationship-views. With a usage scenario, we demonstrate the usefulness of SightBi for sensemaking of cross-view data relationships.

MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation.

We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the needs of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system.

Modeling layout design for multiple-view visualization via Bayesian inference

Modeling layout design for multiple-view visualization via Bayesian inference

Composition and Configuration Patterns in Multiple-View Visualizations.

Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

Comparing Color and Leader Line Highlighting Strategies in Coordinated View Geovisualizations.

In most coordinated view geovisualization tools, a transient visual effect is used to highlight observations across views when brushed with a mouse or other input device. Most current geovisualization and information visualization systems use colored outlines or fills to highlight observations, but there remain a wide range of alternative visual strategies that can also be implemented and compared to color highlighting to evaluate user performance. This paper describes the results of an experiment designed to compare user performance with two highlighting methods; color and leader lines. Our study methodology uses eye-tracking to capture participant eye fixations while they answer questions that require attention to highlighted observations in multiple views. Our results show that participants extract information as efficiently from coordinated view displays that use leader line highlighting to link information as they do from those that use a specific color to highlight items. We also found no significant differences when changing the color of the highlighting effect from red to black. We conclude that leader lines show significant potential for use as an alternative highlighting method in coordinated multiple view visualizations, allowing color to be reserved for representing thematic attributes of data.

Multiple-view strategies for enhanced understanding of dynamic tourist activity through geovisualization at regional and national scales

This article is part of research exploring various ways to visualize the complexity of sentient movement in space and time. In this case, visualization focuses on the movement of tourists on the West Coast of the South Island of New Zealand, with some commentary on national tourist activity. Working at the atomistic level of the individual we adopt space, time, and activity (s,t,a) as the three main dimensions of sentient movement, but we emphasize activity as a privileged dimension where visualization is involved. The rationale for this is that when interpreting movements of individuals or crowds, most itineraries either reflect resources in the environment that encourage travel or avoid risk or cause delay, resources ranging from food and water to incoming sunshine or rain. Activity and process is at the heart of most rich movement data-sets, and we suggest that using multiple-view visualization to decode (s,t,a) and environmental patterns opens further avenues for interpreting and communicating dynamic human and animal geographies. To this end, this article focuses on investigating multiple-view geovisualizations through the lens of six groups of multiple views, each of them demonstrating data in a unique way, in order to reveal complex (s,t,a) patterns of rich tourist movement data. An insight-based domain expert user evaluation is discussed at the end of this article.

A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

Visual Exploration and Analysis of Historic Hotel Visits

Understanding the spatial and temporal characteristics of individual and group behavior in social networks is a critical component of visual tools for intelligence analysis, emergency management, consumer analysis, and human geography. Identification and analysis of patterns of recurring events is an essential feature of such tools. In this paper, we describe an interactive visual tool for exploring the visitation patterns of guests at two hotels in central Pennsylvania from 1894 to 1900. The centerpiece of the tool is a wrapping spreadsheet technique, called reruns, that reveals regular and irregular periodic patterns of events in multiple overlapping artificial and natural calendars. Implemented as a coordinated multiple view visualization in Improvise, the tool is in ongoing development through an iterative process of data collection, transcription, hypothesis, design, discovery, analysis, and evaluation in close collaboration with historical geographers. Numerous discoveries have driven additional data collection from archival newspaper and census sources, as well as plans to enhance analysis of spatial patterns using historic weather records and railroad schedules. Distributed online evaluations of usability and usefulness have resulted in feature and design recommendations that are being incorporated into the tool.

A Model and Software System for Coordinated and Multiple Views in Exploratory Visualization

This paper describes a model for expressing coordination in multiple view visualization systems. We present the model and describe a prototype implementation that illustrates the features of the model. Current visualization systems tend to have an informal and inconsistent approach to coordination. Our model takes a formal approach to describing widely used coordination concepts. The model is based on views sharing abstract objects such as the visualization parameters of the dataflow model. Additionaly, this paper describes how current coordinations in exploratory visualization work and how novel coordinations can be constructed using our model.

Exploring Cognitive Strategies for Integrating Multiple-View Visualizations

Visualizing information is useful for finding patterns in complex data sets, but little research has been done on how people understand multiple-view visualizations (multiple visualizations presented simultaneously). A controlled experiment was performed using different combinations of visualizations and different task types as independent variables, and qualitative and quantitative data were collected. To collect the data psychological tests, logs of the participants' interaction, eye-tracking equipment, and video recordings were used. This paper reports a portion of the results from this experiment. Main findings include that, contrary to what was suggested in previous literature, the time cost for switching between different types of visualizations (context switching) may not be significant, and that displaying the data using the same type of visualization may cause interference. Orthogonal combinations appear to aid users in finding and recognizing patterns, and focusing attention and analogical reasoning on spatial relationships may be important cognitive abilities for the given tasks.

Visualization Schemas and a Web-Based Architecture for Custom Multiple-View Visualization of Multiple-Table Databases

Relational databases provide significant flexibility to organize, store, and manipulate an infinite variety of complex data collections. This flexibility is enabled by the concept of relational data Schemas, which allow data owners to easily design custom databases according to their unique needs. However, user interfaces and information visualizations for accessing and utilizing databases have not kept pace with this level of flexibility. Visualizations need to integrate multiple tables and diverse visualization tools into custom solutions. This paper describes advances to Snap-Together Visualization, introduces Visualization Schemas, and presents an extensible system architecture. The Snap model for custom multiple-view visualization establishes an analogy to the relational data model, enabling coordinated data design and visualization design. Visualization Schemas are a natural extension to data Schemas, and provide a user interface that enables data owners to rapidly construct and disseminate custom visualizations without programming. The web-based software architecture supports run-time extensibility, enabling end-user integration and dissemination of diverse data and visualization tools from the field.