一种层次结构中多维属性的可视化方法

在很多领域的统计分析中,通常需要分析既具有层次结构又具有多维属性的复杂数据,如食品安全数据、股票数据、网络安全数据等.针对现有多维数据和层次结构的可视化方法不能满足对同时具有层次和多维两种属性数据的可视分析要求,提出了一种树图中的多维坐标MCT(multi-coordinate in treemap)技术.该技术采用基于Squarified和Strip布局算法的树图表示层次结构,用树图中节点矩形的边作为属性轴,通过属性映射、属性点连接、曲线拟合实现层次结构中多维属性的可视化.将该技术应用于全国农药残留侦测数据,实现了对全国各地区、各超市、各农产品中农药残留检出和超标情况的可视化,为领域人员提供了有效的分析工具.MCT技术也可用于其他领域的层次多属性数据的可视化.

This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.

A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems

The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects.Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology.It is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing strategies.However,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly's multi-level hierarchies.This paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a supercomputer.We coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system configurations.Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis tasks.We demonstrate the effectiveness of the system with a set of case studies.Our system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.

A visual analytics design for studying rhythm patterns from human daily movement data

Human’s daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms.Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners,transportation analysts,to business strategists.In this paper,we present an interactive visual analytics design for understanding and utilizing data collected from tracking human’s movements.The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time.Case studies using real-world human movement data,including massive urban public transportation data in Singapore and the MIT reality mining dataset,and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.

Comparative visual analytics for assessing medical records with sequence embedding

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence.However,such analysis is not straightforward due to the characteristics of medical records:high dimensionality,irregularity in time,and sparsity.To address this challenge,we introduce a method for similarity calculation of medical records.Our method employs event and sequence embeddings.While we use an autoencoder for the event embedding,we apply its variant with the self-attention mechanism for the sequence embedding.Moreover,in order to better handle the irregularity of data,we enhance the self-attention mechanism with consideration of different time intervals.We have developed a visual analytics system to support comparative studies of patient records.To make a comparison of sequences with different lengths easier,our system incorporates a sequence alignment method.Through its interactive interface,the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records.We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

Concise provenance of interactive network analysis

Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.

Analyzing gaze behavior for text-embellished narrative visualizations under different task scenarios

We conduct an eye tracking study to investigate perception text-embellished narrative visualizations under different task conditions.Study stimuli are data visualizations embellished with text-based elements:annotations,captions,labels,and descriptive text.We consider three common viewing tasks that occur when these types of graphics are viewed:(1)simple observation,(2)active search to answer a query,and(3)information memorization for later recall.The overarching goal is to understand,at a perceptual level,if and how task affects how these visualizations are interacted with.By analyzing collected gaze data and conducting advanced semantic scanpath analysis,we find,at a high level,diverse patterns of gaze behavior:simple observation and information memorization lead to similar optical viewing strategies,while active search significantly diverges,both in regards to which areas of the visualization are focused upon and how often embellishments are interacted with.We discuss study outcomes in the context of embellishing visualizations with text for various usage scenarios.

VizSEC 2007

网络化计算机无处不在，并且经受着攻击、误用、乱用。自动化系统与这种威胁作战是一种可能的解决方案，但是大多数自动化系统要求有监督，其实解除网络威胁的一种方法是向安全分析员提供较好的工具，以便发现新模式、检测异常、鉴别相互关系、交流最新发现。

Foreword to the Special Issue on PacificVis 2020 Workshop on Visualization Meets AI

The task of data visualization generally involves a design step,which requires the knowledge of the data domain and visualiza-tion methods to do well.Because of the immense space for design optimization,it can take both novices and experts a tremendous effort to derive desired visualization results from data for explo-ration or communication.Following the resurgence of artificial intelligence technology in recent years,in the field of visualiza-tion,there is the growing interest and opportunity in applying AI to perform data transformation and to assist the generation of visualization,aiming to strike a balance between cost and quality.The use of visualization to enhance AI is the other active line of research.The PacificVis 2020 Workshop on Visualization Meets AI aims at exploring this emerging area of research and practice by fostering communication between visualization researchers and practitioners.This issue of Visual Informatics features the six papers chosen by the Workshop.