This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Vi...This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Visual analytics were used to examine the impact of various climate change indicators on different aspects of human development. The study highlights the urgent need for climate change action and encourages policymakers to make decisive moves. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that regions with higher CO2 emissions experience a significantly higher incidence of life-threatening diseases compared to regions with lower emissions. Additionally, higher CO2 emissions correlate with consistent death rates. Increased pollution exposure is associated with a higher prevalence of life-threatening diseases and higher rates of malnutrition. Moreover, greater mineral depletion is linked to more frequent life-threatening diseases, suggesting that industrialization contributes to adverse health effects. These results provide valuable insights for policy and decision-making aimed at mitigating the impact of climate change on human development.展开更多
In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect ...In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network.The technique can help answer questions,such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output.Our visual analytics approach comprises several components:First,our input visualization shows the input sequence and how it relates to the output(using color coding).In addition,hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states.Trajectories are also employed to show the details of the evolution of the hidden state configurations.Finally,a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers,and a histogram indicates the distances between the hidden states within the original space.The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences.To demonstrate the capability of our approach,we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.展开更多
For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the ev...For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.展开更多
With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have ...With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry.In this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual Analytics.AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively.The code is available at https://github.com/antvis/AVA.展开更多
Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most sta...Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end users.Some previous studies attempted to“open the black box”and increase the interpretability of generated predictions.However,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging.To overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning.The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels.Based on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns.Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.展开更多
Traffic congestion is becoming increasingly severe as a result of urbanization,which not only impedes people’s ability to travel but also hinders the economic development of cities.Modeling the correlation between co...Traffic congestion is becoming increasingly severe as a result of urbanization,which not only impedes people’s ability to travel but also hinders the economic development of cities.Modeling the correlation between congestion and its influencing factors using machine learning methods makes it possible to quickly identify congested road segments.Due to the intrinsic black-box character of machine learning models,it is difficult for experts to trust the decision results of road congestion prediction models and understand the significance of congestion-causing factors.In this paper,we present a model interpretability method to investigate the potential causes of traffic congestion and quantify the importance of various influencing factors using the SHAP method.Due to the multidimensionality of these factors,it can be challenging to visually represent the impact of all factors.In response,we propose TCEVis,an interactive visual analytics system that enables multi-level exploration of road conditions.Through three case studies utilizing actual data,we demonstrate that the TCEVis system offers advantages for assisting traffic managers in analyzing the causes of traffic congestion and elucidating the significance of various influencing factors.展开更多
Influence maximization(IM)algorithms play a significant role in hypergraph analysis tasks,such as epidemic control analysis,viral marketing,and social influence analysis,and various IM algorithms have been proposed.Th...Influence maximization(IM)algorithms play a significant role in hypergraph analysis tasks,such as epidemic control analysis,viral marketing,and social influence analysis,and various IM algorithms have been proposed.The main challenge lies in IM algorithm evaluation,due to the complexity and diversity of the spreading processes of different IM algorithms in different hypergraphs.Existing evaluation methods mainly leverage statistical metrics,such as influence spread,to quantify overall performance,but do not fully unravel spreading characteristics and patterns.In this paper,we propose an exploratory visual analytics system,IMVis,to assist users in exploring and evaluating IM algorithms at the overview,pattern,and node levels.A spreading pattern mining method is first proposed to characterize spreading processes and extract important spreading patterns to facilitate efficient analysis and comparison of IM algorithms.Novel visualization glyphs are designed to comprehensively reveal both temporal and structural features of IM algorithms’spreading processes in hypergraphs at multiple levels.The effectiveness and usefulness of IMVis are demonstrated through two case studies and expert interviews.展开更多
Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-o...Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.展开更多
We propose interest-driven progressive visual analytics.The core idea is to filter samples with features of interest to analysts from the given dataset for analysis.The approach relies on a generative model(GM)trained...We propose interest-driven progressive visual analytics.The core idea is to filter samples with features of interest to analysts from the given dataset for analysis.The approach relies on a generative model(GM)trained using the given dataset as the training set.The GM characteristics make it convenient to find ideal generated samples from its latent space.Then,we filter the original samples similar to the ideal generated ones to explore patterns.Our research involves two methods for achieving and applying the idea.First,we give a method to explore ideal samples from a GM’s latent space.Second,we integrate the method into a system to form an embedding-based analytical workflow.Patterns found on open datasets in case studies,results of quantitative experiments,and positive feedback from experts illustrate the general usability and effectiveness of the approach.展开更多
Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks,which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.Despite en...Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks,which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.Despite enhancing robustness,adversarial training often results in a trade-off with standard accuracy on normal data,a phenomenon that remains a contentious issue.In addition,the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve.This paper introduces ATVis,a visual analytics framework for examining and diagnosing adversarial training processes.Through multi-level visualization design,ATVis enables the examination of model robustness from various granularity,facilitating a detailed understanding of the dynamics in the training epochs.The framework reveals the complex relationship between adversarial robustness and standard accuracy,which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training.The effectiveness of the framework is demonstrated through case studies.展开更多
A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future pred...A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.展开更多
Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization.To better identify which research topics are promising and to learn how to apply relevant tech...Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization.To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics,we systematically review259 papers published in the last ten years together with representative works before 2010.We build a taxonomy,which includes three first-level categories:techniques before model building,techniques during modeling building,and techniques after model building.Each category is further characterized by representative analysis tasks,and each task is exemplified by a set of recent influential works.We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.展开更多
Visual analytics employs interactive visualizations to integrate users＇ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in m...Visual analytics employs interactive visualizations to integrate users＇ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to acld the current understanding and better understand research trends in the field.展开更多
Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remark...Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities.To promote further academic research and assist the development of industrial urban analytics systems,we comprehensively review urban visual analytics studies from four perspectives.In particular,we identify 8 urban domains and 22 types of popular visualization,analyze 7 types of computational method,and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.We conclude with potential research directions and opportunities.展开更多
Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the re...Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans.展开更多
GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.Howeve...GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.展开更多
Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as a...Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as an important source for understanding learners activities and opinions.However,the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern.In this paper,we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums.This tool,called MessageLens,aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets:discussion topic,learner attitude,and communication among learners.With various visualization tools,instructors can investigate learner activities from different perspectives.We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system.Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people.展开更多
The word‘pattern’frequently appears in the visualisation and visual analytics literature,but what do we mean when we talk about patterns?We propose a practicable definition of the concept of a pattern in a data dist...The word‘pattern’frequently appears in the visualisation and visual analytics literature,but what do we mean when we talk about patterns?We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole.Our theoretical model describes how patterns are made by relationships existing between data elements.Knowing the types of these relationships,it is possible to predict what kinds of patterns may exist.We demonstrate how our model underpins and refines the established fundamental principles of visualisation.The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns,once discovered,are explicitly involved in further data analysis.展开更多
Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a mul...Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.展开更多
The ever-increasing amount of major security incidents has led to an emerging interest in cooperative approaches to encounter cyber threats.To enable cooperation in detecting and preventing attacks it is an inevitable...The ever-increasing amount of major security incidents has led to an emerging interest in cooperative approaches to encounter cyber threats.To enable cooperation in detecting and preventing attacks it is an inevitable necessity to have structured and standardized formats to describe an incident.Corresponding formats are complex and of an extensive nature as they are often designed for automated processing and exchange.These characteristics hamper the readability and,therefore,prevent humans from understanding the documented incident.This is a major problem since the success and effectiveness of any security measure rely heavily on the contribution of security experts.To meet these shortcomings we propose a visual analytics concept enabling security experts to analyze and enrich semi-structured cyber threat intelligence information.Our approach combines an innovative way of persisting this data with an interactive visualization component to analyze and edit the threat information.We demonstrate the feasibility of our concept using the Structured Threat Information eXpression,the state-ofthe-art format for reporting cyber security issues.展开更多
文摘This study explores the complex relationship between climate change and human development. The aim is to understand how climate change affects human development across countries, regions, and the global population. Visual analytics were used to examine the impact of various climate change indicators on different aspects of human development. The study highlights the urgent need for climate change action and encourages policymakers to make decisive moves. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that regions with higher CO2 emissions experience a significantly higher incidence of life-threatening diseases compared to regions with lower emissions. Additionally, higher CO2 emissions correlate with consistent death rates. Increased pollution exposure is associated with a higher prevalence of life-threatening diseases and higher rates of malnutrition. Moreover, greater mineral depletion is linked to more frequent life-threatening diseases, suggesting that industrialization contributes to adverse health effects. These results provide valuable insights for policy and decision-making aimed at mitigating the impact of climate change on human development.
基金Funded by the Deutsche Forschungsgemeinschaft(German Research Foundation),No.251654672—TRR 161(Project B01)Germany’s Excellence Strategy,No.EXC-2075—390740016.
文摘In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network.The technique can help answer questions,such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output.Our visual analytics approach comprises several components:First,our input visualization shows the input sequence and how it relates to the output(using color coding).In addition,hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states.Trajectories are also employed to show the details of the evolution of the hidden state configurations.Finally,a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers,and a histogram indicates the distances between the hidden states within the original space.The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences.To demonstrate the capability of our approach,we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.
基金supported by Natural Science Foundation of China(NSFC No.62472099 and No.62202105)Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr Institute for Machine Learning and Artificial Intelligence(Lamarr22B)by EU in project CrexData(grant agreement No.101092749).
文摘For event analysis,the information from both before and after the event can be crucial in certain scenarios.By incorporating a contextualized perspective in event analysis,analysts can gain deeper insights from the events.We propose a contextualized visual analysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events.The framework consists of a design of visual representation for multivariate event contexts,a data processing workflow to support the visualization,and a context-centered visual analysis system to facilitate the interactive exploration of temporal patterns.To demonstrate the applicability and effectiveness of our framework,we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts.
基金National Natural Science Foundation of China(62132017)Zhejiang Provincial Natural Science Foundation of China(LD24F020011).
文摘With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry.In this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual Analytics.AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively.The code is available at https://github.com/antvis/AVA.
基金supported in part by a Grant in-Aid for Scientific Research B(22H03573)of the Japan Society for the Promotion of Science(JSPS)in part by the National Natural Science Foundation of China(92067109,61873119)+1 种基金in part by Shenzhen Science and Technology Program(ZDSYS20210623092007023,GJHZ20210705141808024)in part by Guangdong Key Program(2021QN02X794)。
文摘Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end users.Some previous studies attempted to“open the black box”and increase the interpretability of generated predictions.However,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging.To overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning.The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels.Based on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns.Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.
基金National Natural Science Foundation of China under grant number 42171450,Key R&D Project of Science and Technology Development Plan of Jilin Province under Grant 20210201074GXNational Natural Science Foundation of China under grant number 62377008.
文摘Traffic congestion is becoming increasingly severe as a result of urbanization,which not only impedes people’s ability to travel but also hinders the economic development of cities.Modeling the correlation between congestion and its influencing factors using machine learning methods makes it possible to quickly identify congested road segments.Due to the intrinsic black-box character of machine learning models,it is difficult for experts to trust the decision results of road congestion prediction models and understand the significance of congestion-causing factors.In this paper,we present a model interpretability method to investigate the potential causes of traffic congestion and quantify the importance of various influencing factors using the SHAP method.Due to the multidimensionality of these factors,it can be challenging to visually represent the impact of all factors.In response,we propose TCEVis,an interactive visual analytics system that enables multi-level exploration of road conditions.Through three case studies utilizing actual data,we demonstrate that the TCEVis system offers advantages for assisting traffic managers in analyzing the causes of traffic congestion and elucidating the significance of various influencing factors.
基金Zhejiang Provincial Natural Science Foundation of China(LQ22F020017)National Natural Science Foundation of China(62302137)Open Project Program of the State Key Lab of CAD&CG of Zhejiang University(A2104).
文摘Influence maximization(IM)algorithms play a significant role in hypergraph analysis tasks,such as epidemic control analysis,viral marketing,and social influence analysis,and various IM algorithms have been proposed.The main challenge lies in IM algorithm evaluation,due to the complexity and diversity of the spreading processes of different IM algorithms in different hypergraphs.Existing evaluation methods mainly leverage statistical metrics,such as influence spread,to quantify overall performance,but do not fully unravel spreading characteristics and patterns.In this paper,we propose an exploratory visual analytics system,IMVis,to assist users in exploring and evaluating IM algorithms at the overview,pattern,and node levels.A spreading pattern mining method is first proposed to characterize spreading processes and extract important spreading patterns to facilitate efficient analysis and comparison of IM algorithms.Novel visualization glyphs are designed to comprehensively reveal both temporal and structural features of IM algorithms’spreading processes in hypergraphs at multiple levels.The effectiveness and usefulness of IMVis are demonstrated through two case studies and expert interviews.
基金supported in part by the Shenzhen Science and Technology Program(No.ZDSYS20210623092007023)in part by the National Natural Science Foundation of China(No.62172398)the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515011700).
文摘Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.
文摘We propose interest-driven progressive visual analytics.The core idea is to filter samples with features of interest to analysts from the given dataset for analysis.The approach relies on a generative model(GM)trained using the given dataset as the training set.The GM characteristics make it convenient to find ideal generated samples from its latent space.Then,we filter the original samples similar to the ideal generated ones to explore patterns.Our research involves two methods for achieving and applying the idea.First,we give a method to explore ideal samples from a GM’s latent space.Second,we integrate the method into a system to form an embedding-based analytical workflow.Patterns found on open datasets in case studies,results of quantitative experiments,and positive feedback from experts illustrate the general usability and effectiveness of the approach.
基金supported in part by the NSFC (62202217,62202244)Guangdong Basic and Applied Basic Research Foundation (No.2023A1515012889)Guangdong Key Program (No.2021QN02X794).
文摘Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks,which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.Despite enhancing robustness,adversarial training often results in a trade-off with standard accuracy on normal data,a phenomenon that remains a contentious issue.In addition,the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve.This paper introduces ATVis,a visual analytics framework for examining and diagnosing adversarial training processes.Through multi-level visualization design,ATVis enables the examination of model robustness from various granularity,facilitating a detailed understanding of the dynamics in the training epochs.The framework reveals the complex relationship between adversarial robustness and standard accuracy,which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training.The effectiveness of the framework is demonstrated through case studies.
基金This work was supported by National Basic Re- search Program of China （973 Program） （2015CB352503）, Major Pro- gram of the National Natural Science Foundation of China （61232012）, the National Natural Science Foundation of China （Grant Nos. 61303141, 61422211, u1536118, u1536119）, Zhejiang Provincial Natural Science Foundation of China （LR13F020001）, the Fundamental Research Funds for the Central Universities, the Innovation Joint Research Center for Cyber- Physical-Society System, and the United State＇s National Science Founda- tion （1350573）.
文摘A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.
基金supported by the National Key R&D Program of China(Nos.2018YFB1004300,2019YFB1405703)the National Natural Science Foundation of China(Nos.61761136020,61672307,61672308,61936002)TC190A4DA/3,the Institute Guo Qiang,Tsinghua University,in part by Tsinghua–Kuaishou Institute of Future Media Data。
文摘Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization.To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics,we systematically review259 papers published in the last ten years together with representative works before 2010.We build a taxonomy,which includes three first-level categories:techniques before model building,techniques during modeling building,and techniques after model building.Each category is further characterized by representative analysis tasks,and each task is exemplified by a set of recent influential works.We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.
基金partly supported by the National Natural Science Foundation of China under Grant No. 61070114the Program for New Century Excellent Talents in University of China under Grant No. NCET-12-1087the Zhejiang Provincial Qianjiang Talents of China under Grant No. 2013R10054
文摘Visual analytics employs interactive visualizations to integrate users＇ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to acld the current understanding and better understand research trends in the field.
基金This work was supported by National Natural Science Foundation of China(62072400)the Collaborative Innovation Center of Artificial Intel-ligence by MOE and Zhejiang Provincial Government(ZJU),and the Zhejiang Lab(2021KE0AC02)。
文摘Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models.Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities.To promote further academic research and assist the development of industrial urban analytics systems,we comprehensively review urban visual analytics studies from four perspectives.In particular,we identify 8 urban domains and 22 types of popular visualization,analyze 7 types of computational method,and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.We conclude with potential research directions and opportunities.
基金This research was funded by National Key R&D Program of China(No.SQ2018YFB100002)the National Natural Science Foundation of China(No.s 61761136020,61672308)+5 种基金Microsoft Research Asia,Fraunhofer Cluster of Excellence on"Cognitive Internet Technologies",EU through project Track&Know(grant agreement 780754)NSFC(61761136020)NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1609217)Zhejiang Provincial Natural Science Foundation(LR18F020001)NSFC Grants 61602306Fundamental Research Funds for the Central Universities。
文摘Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans.
基金supported by the National Key Research&Development Program of China(2017YFB0202203)National Nat-ural Science Foundation of China(61472354,61672452)NSFCGuangdong Joint Fund,China(U1611263).
文摘GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system.
文摘Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as an important source for understanding learners activities and opinions.However,the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern.In this paper,we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums.This tool,called MessageLens,aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets:discussion topic,learner attitude,and communication among learners.With various visualization tools,instructors can investigate learner activities from different perspectives.We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system.Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people.
基金This research was supported by Fraunhofer Center for Machine Learning within the Fraunhofer Cluster for Cognitive Internet Technologiesby DFG within Priority Programme 1894(SPP VGI)+2 种基金by EU in project SoBigData++by SESAR in projects TAPAS and SIMBADby Austrian Science Fund(FWF)project KnowVA(grant P31419-N31).
文摘The word‘pattern’frequently appears in the visualisation and visual analytics literature,but what do we mean when we talk about patterns?We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole.Our theoretical model describes how patterns are made by relationships existing between data elements.Knowing the types of these relationships,it is possible to predict what kinds of patterns may exist.We demonstrate how our model underpins and refines the established fundamental principles of visualisation.The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns,once discovered,are explicitly involved in further data analysis.
基金Partial support for this research was provided by the US National Science Foundation (Nos. 1050477, 0959979, and 1117132)by a Brookhaven National Lab LDRD grant+2 种基金by the US Department of Energy (DOE) Office of Basic Energy Sciences, Division of Chemical Sciences, GeosciencesBiosciences and by the IT Consilience Creative Project through the Ministry of Knowledge Economy, Republic of Korea national scientific user facility sponsored by the DOE's OBER at Pacific Northwest National Laboratory (PNNL)PNNL is operated by the US DOE by Battelle Memorial Institute under contract No.DE-AC06-76RL0 1830
文摘Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.
基金supported by the Federal Ministry of Education and Research,Germany,as part of the BMBF DINGfest project。
文摘The ever-increasing amount of major security incidents has led to an emerging interest in cooperative approaches to encounter cyber threats.To enable cooperation in detecting and preventing attacks it is an inevitable necessity to have structured and standardized formats to describe an incident.Corresponding formats are complex and of an extensive nature as they are often designed for automated processing and exchange.These characteristics hamper the readability and,therefore,prevent humans from understanding the documented incident.This is a major problem since the success and effectiveness of any security measure rely heavily on the contribution of security experts.To meet these shortcomings we propose a visual analytics concept enabling security experts to analyze and enrich semi-structured cyber threat intelligence information.Our approach combines an innovative way of persisting this data with an interactive visualization component to analyze and edit the threat information.We demonstrate the feasibility of our concept using the Structured Threat Information eXpression,the state-ofthe-art format for reporting cyber security issues.
