Xiaotong Liu

ViSizer: A Visualization Resizing Framework

Visualization resizing is useful for many applications where users may use different display devices. General resizing techniques (e.g., uniform scaling) and image-resizing techniques suffer from several drawbacks, as they do not consider the content of the visualizations. This work introduces ViSizer, a perception-based framework for automatically resizing a visualization to fit any display. We formulate an energy function based on a perception model (feature congestion), which aims to determine the optimal deformation for every local region. We subsequently transform the problem into an optimization problem by the energy function. An efficient algorithm is introduced to iteratively solve the problem, allowing for automatic visualization resizing.

Association Analysis for Visual Exploration of Multivariate Scientific Data Sets

The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the datas multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

CompactMap: A mental map preserving visual interface for streaming text data

As text streams become increasingly available from social media such as Facebook and Twitter, visual analysis of streaming text data is playing an important role in most business sectors. A fundamental challenge in visualizing a large amount of streaming text data is to preserve the users mental map to enable tracking dynamic changes in topics, while simultaneously utilizing the display space efficiently. In this paper, we present CompactMap, an online visual interface that packs text clusters efficiently, with stable updates to maintain the users mental map. It achieves spatiotemporally coherent layouts by dynamically matching clusters across time, and removing cluster overlaps according to spatial proximity and constraints. We developed a visual search engine based on CompactMaps for exploring a large amount of text streams in details on demand. We demonstrate the effectiveness of our approach in a controlled user study compared with a competing method.

CorrelatedMultiples: Spatially Coherent Small Multiples With Constrained Multi-Dimensional Scaling

Displaying small multiples is a popular method for visually summarizing and comparing multiple facets of a complex data set. If the correlations between the data are not considered when displaying the multiples, searching and comparing specific items become more difficult since a sequential scan of the display is often required. To address this issue, we introduce CorrelatedMultiples, a spatially coherent visualization based on small multiples, where the items are placed so that the distances reflect their dissimilarities. We propose a constrained multi‐dimensional scaling (CMDS) solver that preserves spatial proximity while forcing the items to remain within a fixed region. We evaluate the effectiveness of our approach by comparing CMDS with other competing methods through a controlled user study and a quantitative study, and demonstrate the usefulness of CorrelatedMultiples for visual search and comparison in three real‐world case studies.

The Effects of Representation and Juxtaposition on Graphical Perception of Matrix Visualization

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.

Supporting multifaceted viewing of word clouds with focus+context display

Word clouds provide an effective way to visually summarize important keywords from a large collection of text. Despite their increasing popularity, relatively less attention has been paid on developing interactive techniques for flexible word cloud navigation and manipulation. In this article, we present a focus + context display technique to support multifaceted viewing of word clouds. In our algorithm, the sizes of words in a word cloud are first changed to reflect the current importance metric selected by the user and then scaled to balance space utilization and word readability. To remove word overlaps caused by changes of sizes while maintaining the positional dependency modeled by a directed acyclic graph, we propose a force-directed model that also maximizes the utilization of display space. We demonstrate the effectiveness and usefulness of our techniques through case studies using a real-world dataset and evaluate the performance of the constraint-based overlap removal method in achieving stable layouts during importance transformation.

Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots

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.

Visualization of Time-Varying Weather Ensembles across Multiple Resolutions

Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose feedback shows the effectiveness of our proposed exploration work-flow.

Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.

A Bayesian approach for probabilistic streamline computation in uncertain flows

Streamline-based techniques play an important role in visualizing and analyzing uncertain steady vector fields. It is a challenging problem to generate accurate streamlines in uncertain vector fields due to the global uncertainty transportation. In this work, we present a novel probabilistic method for streamline computation on uncertain steady vector fields using a Bayesian framework. In our framework, a streamline is modeled as a state space model which captures the spatial coherence of integration steps and uncertainty in local distributions using the conditional prior density and the likelihood function. To approximate the posterior distribution for all the possible traces originating from a given seed position, a set of weighted samples are iteratively updated from which streamlines with higher likelihood can be derived. We qualitatively and quantitatively compare our method with alternative methods on different types of flow field data sets. Our method can generate possible streamlines with higher certainty and hence more accurate flow traces.