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
Audit logs are different from other software logs in that they record the most primitive events(i.e.,system calls)in modem operating systems.Audit logs contain a detailed trace of an operating system,and thus have rec...Audit logs are different from other software logs in that they record the most primitive events(i.e.,system calls)in modem operating systems.Audit logs contain a detailed trace of an operating system,and thus have received great attention from security experts and system administrators.However,the complexity and size of audit logs,which increase in real time,have hindered analysts from understanding and analyzing them.In this paper,we present a novel visual analytics system,LongLine,which enables interactive visual analyses of large-scale audit logs.LongLine lowers the interpretation barrier of audit logs by employing human-understandable representations(e.g.,file paths and commands)instead of abstract indicators of operating systems(e.g.,file descriptors)as well as revealing the temporal patterns of the logs in a multi-scale fashion with meaningful granularity of time in mind(e.g.,hourly,daily,and weekly).LongLine also streamlines comparative analysis between interesting subsets of logs,which is essential in detecting anomalous behaviors of systems.In addition,LongLine allows analysts to monitor the system state in a streaming fashion,keeping the latency between log creation and visualization less than one minute.Finally,we evaluate our system through a case study and a scenario analysis with security experts.展开更多
Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key i...Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key information from massive text data.Topic modeling,as one of the novel techniques that extracts a thematic structure from documents,is widely used to generate text summarization and foster an overall understanding of the corpus content.Although powerful,this technique may not be directly applicable for general analytics scenarios since the topics and topic-document relationship are often presented probabilistically in models.Moreover,information that plays an important role in knowledge discovery,for example,times and authors,is hardly reflected in topic modeling for comprehensive analysis.In this paper,we address this issue by presenting a visual analytics system,VISTopic,to help users make sense of large document collections based on topic modeling.VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model(HLTM)(Liu et al.,2014).In specific,a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization.To leverage multi-perspective information for visual analytics,VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents.Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.展开更多
Consumer credit risk analysis plays a significant role in stabilizing a bank＇s investments and in maximizing its profits. As a large financial institution, Bank of America relies on effective risk analyses to minimiz...Consumer credit risk analysis plays a significant role in stabilizing a bank＇s investments and in maximizing its profits. As a large financial institution, Bank of America relies on effective risk analyses to minimize the net credit loss resulting from its credit products （e.g., mortgage and credit card loans）. Due to the size and complexity of the data involved in this process, analysts are facing challenges in monitoring the data, comparing its geospatial and temporal patterns, and developing appropriate strategies based on the correlation from multiple analysis perspectives. To address these challenges, we present RiskVA, an interactive visual analytics system that is tailored to support credit risk analysis. RiskVA provides interactive data exploration and correlation, and visually facilitates depictions of market fluctuations and temporal trends for a targeted credit product. When evaluated by analysts from Bank of America, RiskVA was appreciated for its effectiveness in facilitating the bank＇s risk management.展开更多
Although traditional Chinese medicine(TCM)and modern medicine(MM)have considerably different treatment philosophies,they both make important contributions to human health care.TCM physicians usually treat diseases usi...Although traditional Chinese medicine(TCM)and modern medicine(MM)have considerably different treatment philosophies,they both make important contributions to human health care.TCM physicians usually treat diseases using TCM formula(TCMF),which is a combination of specific herbs,based on the holistic philosophy of TCM,whereas MM physicians treat diseases using chemical drugs that interact with specific biological molecules.The difference between the holistic view of TCM and the atomistic view of MM hinders their combination.Tools that are able to bridge together TCM and MM are essential for promoting the combination of these disciplines.In this paper,we present TCMFVis,a visual analytics system that would help domain experts explore the potential use of TCMFs in MM at the molecular level.TCMFVis deals with two significant challenges,namely,(i)intuitively obtaining valuable insights from heterogeneous data involved in TCMFs and(ii)efficiently identifying the common features among a cluster of TCMFs.In this study,a four-level(herb-ingredient-targetdisease)visual analytics framework was designed to facilitate the analysis of heterogeneous data in a proper workflow.Several set visualization techniques were first introduced into the system to facilitate the identification of common features among TCMFs.Case studies on two groups of TCMFs clustered by function were conducted by domain experts to evaluate TCMFVis.The results of these case studies demonstrate the usability and scalability of the system.展开更多
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 Dra...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.展开更多
The television ratings provide an effective way to analyze the popularity of TV programs and audiences’watching habits.Most previous studies have analyzed the ratings from a single perspective.Few efforts have integr...The television ratings provide an effective way to analyze the popularity of TV programs and audiences’watching habits.Most previous studies have analyzed the ratings from a single perspective.Few efforts have integrated analysis from different perspectives and explored the reasons for changes in ratings.In this paper,we design a visual analysis system called TVseer to analyze audience ratings from three perspectives:TV channels,TV programs,and audiences.The system can help users explore the factors that affect ratings,and assist them in decisions about program productions and schedules.There are six linked views in TVseer:the channel ratings view and program ratings view show ratings change information from the perspective of TV channels and programs respectively;the overlapping program competition view and the same-type program competition view indicate the competitive relationships among programs;the audience transfer view shows how audiences are moving among different channels;the audience group view displays audience groups based on their watching behavior.Besides,we also construct case studies and expert interviews to prove our system is useful and effective.展开更多
To effectively track the impact of population migration between regions on the spread of infectious diseases, this paper proposes a visualized analysis and prediction system of infectious diseases based on the improve...To effectively track the impact of population migration between regions on the spread of infectious diseases, this paper proposes a visualized analysis and prediction system of infectious diseases based on the improved SIR model. The research contents including: using the multi graph link interaction mode, visualizing the space-time distribution and development trend of infectious diseases;The LightGBM model is used to track the changes of infection rate and recovery rate, and the Mi/Mo SIR model is constructed according to the initial data of different populations;Mi/Mo SIR model is used to predict infectious diseases in combination with visual panel, providing users with tools to analyze and explain the space-time characteristics and potential laws of infectious diseases. The study found that the closure of cities and the restriction of personnel mobility were necessary and effective, and the system provided an important basis for the prediction and early warning of infectious diseases.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
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
As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynami...As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.展开更多
While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph...While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases： the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.展开更多
Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their uni...Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.展开更多
The regional industry network(RIN)is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region.For most i...The regional industry network(RIN)is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region.For most investors and financial analysts lacking extensive experience,the decision-support information provided by industry networks may be too vague.Conversely,RINs express more detailed and specific industry connections both within and out-side the region.As RIN analysis is domain-specific and current financial network analysis tools are designed for gener-alized analytical tasks and cannot be directly applied to RINs,new visual analysis approaches are needed to enhance information exploration efficiency.In this study,we collaborated with domain experts and proposed V4RIN,an inter-active visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data.Through multiple views in the system panel,users can comprehensively explore the structure,geographical distribution,and spatiotemporal variations of the RIN.Two case studies were con-ducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.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
Mutual funds are one of the most important and popular investment ways for ordinary investors to maintain and increase the value of their assets.However,it is challenging for ordinary investors to select optimal mutua...Mutual funds are one of the most important and popular investment ways for ordinary investors to maintain and increase the value of their assets.However,it is challenging for ordinary investors to select optimal mutual funds from thousands of fund choices managed by different managers.Various investors often have different personal investment preferences and it is difficult to characterize their preferences quickly.Also,mutual fund performance relies on various factors(e.g.,the economic market and the management of fund managers),and most of these factors are dynamically changing,making it difficult to efficiently compare different mutual funds in detail.To address these challenges,we propose FundSelector,an interactive multi-view visual analytics system that quantifies user preferences to rank mutual funds and allows ordinary investors to explore mutual fund performance in terms of multiple factors and scales.Two novel visual designs are proposed to enable detailed comparisons of mutual funds.Rank-informed bipartite contribution bar chart provides interpretable fund ranking results by explicitly showing both positive and negative factors.Elastic trend chart allows investors to analyze and compare the temporal evolution of the mutual funds’performances in a customizable way.We evaluated FundSelector through two case studies and interviews with eight ordinary investors.The results highlight its effectiveness and utility.展开更多
Visual analytics focuses on amplifying users’reasoning and understanding by enhancing data analysis procedures with the efficient incorporation of information visualization and data processing techniques.In this stud...Visual analytics focuses on amplifying users’reasoning and understanding by enhancing data analysis procedures with the efficient incorporation of information visualization and data processing techniques.In this study,we conduct an overview of this multidisciplinary field,focusing on both the process that formalizes its primary concepts and the affiliated research areas.We identify key developments in each area,as well as the challenges that arise when these areas are interconnected under the visual analytics process.We consider that to address the identified challenges,an appropriate representation of key user needs is essential.Therefore,inspired by human-centered design and its principles,we propose a novel methodological approach comprising a human-centered definition of visual analytics that expands on models of the field and quantifies the intermediate states of a data analysis.In addition to the theoretical aspects of the definition,we also provide a set of directions that align the process with technical aspects of the development cycle.In this respect,our research endeavor aims to transform the visual analytics process into an essential method for both conceptualizing data analysis systems capable of anticipating user needs and for streamlining their technical implementation.展开更多
Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and us...Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.展开更多
With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research fi...With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research field,researchers often hope to quickly learn what is being studied in this field,which research points are receiving high attention,which researchers are studying these research points,and then consider the possibility of collaborating with core researchers on these research points.In addition,students who are preparing for academic further education usually conduct research on mentors and mentors’research platforms,including academic connections,employment opportunities,etc.In order to satisfy these requirements,we(1)design a research point state map based on a science map to help researchers and students understand the development state of a new research field;(2)design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors;(3)designs citation pattern histogram to quickly discover research achievements with high research value,such as the Sleeping Beauty papers,recently hot papers,classic papers and so on.Finally,an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data,and its effectiveness is verified through case studies.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
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 work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea govem-ment(MSIP)(No.NRF-2016R1A2B2007153)by the Han-kuk University of Foreign Studies Research Fund.
文摘Audit logs are different from other software logs in that they record the most primitive events(i.e.,system calls)in modem operating systems.Audit logs contain a detailed trace of an operating system,and thus have received great attention from security experts and system administrators.However,the complexity and size of audit logs,which increase in real time,have hindered analysts from understanding and analyzing them.In this paper,we present a novel visual analytics system,LongLine,which enables interactive visual analyses of large-scale audit logs.LongLine lowers the interpretation barrier of audit logs by employing human-understandable representations(e.g.,file paths and commands)instead of abstract indicators of operating systems(e.g.,file descriptors)as well as revealing the temporal patterns of the logs in a multi-scale fashion with meaningful granularity of time in mind(e.g.,hourly,daily,and weekly).LongLine also streamlines comparative analysis between interesting subsets of logs,which is essential in detecting anomalous behaviors of systems.In addition,LongLine allows analysts to monitor the system state in a streaming fashion,keeping the latency between log creation and visualization less than one minute.Finally,we evaluate our system through a case study and a scenario analysis with security experts.
基金This project is funded by a grant proposal(Ref:YBCB2009041-44)of Huawei Technologies Noah’s Ark Lab.
文摘Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key information from massive text data.Topic modeling,as one of the novel techniques that extracts a thematic structure from documents,is widely used to generate text summarization and foster an overall understanding of the corpus content.Although powerful,this technique may not be directly applicable for general analytics scenarios since the topics and topic-document relationship are often presented probabilistically in models.Moreover,information that plays an important role in knowledge discovery,for example,times and authors,is hardly reflected in topic modeling for comprehensive analysis.In this paper,we address this issue by presenting a visual analytics system,VISTopic,to help users make sense of large document collections based on topic modeling.VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model(HLTM)(Liu et al.,2014).In specific,a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization.To leverage multi-perspective information for visual analytics,VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents.Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.
文摘Consumer credit risk analysis plays a significant role in stabilizing a bank＇s investments and in maximizing its profits. As a large financial institution, Bank of America relies on effective risk analyses to minimize the net credit loss resulting from its credit products （e.g., mortgage and credit card loans）. Due to the size and complexity of the data involved in this process, analysts are facing challenges in monitoring the data, comparing its geospatial and temporal patterns, and developing appropriate strategies based on the correlation from multiple analysis perspectives. To address these challenges, we present RiskVA, an interactive visual analytics system that is tailored to support credit risk analysis. RiskVA provides interactive data exploration and correlation, and visually facilitates depictions of market fluctuations and temporal trends for a targeted credit product. When evaluated by analysts from Bank of America, RiskVA was appreciated for its effectiveness in facilitating the bank＇s risk management.
基金supported by National Key R and D Program of China(under Grant No.2016YFA0502304)Important Drug Development Fund,Ministry of Science and Technology of China(2018ZX09735002).
文摘Although traditional Chinese medicine(TCM)and modern medicine(MM)have considerably different treatment philosophies,they both make important contributions to human health care.TCM physicians usually treat diseases using TCM formula(TCMF),which is a combination of specific herbs,based on the holistic philosophy of TCM,whereas MM physicians treat diseases using chemical drugs that interact with specific biological molecules.The difference between the holistic view of TCM and the atomistic view of MM hinders their combination.Tools that are able to bridge together TCM and MM are essential for promoting the combination of these disciplines.In this paper,we present TCMFVis,a visual analytics system that would help domain experts explore the potential use of TCMFs in MM at the molecular level.TCMFVis deals with two significant challenges,namely,(i)intuitively obtaining valuable insights from heterogeneous data involved in TCMFs and(ii)efficiently identifying the common features among a cluster of TCMFs.In this study,a four-level(herb-ingredient-targetdisease)visual analytics framework was designed to facilitate the analysis of heterogeneous data in a proper workflow.Several set visualization techniques were first introduced into the system to facilitate the identification of common features among TCMFs.Case studies on two groups of TCMFs clustered by function were conducted by domain experts to evaluate TCMFVis.The results of these case studies demonstrate the usability and scalability of the system.
基金This research was sponsored by the Advanced Scientific Computing Research Program,the Office of Science,U.SDepartment of Energy through grants DE-SC0014917,DE-SC0012610,and DE-AC02-06CH11357.
文摘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.
基金the Natural Science Foundation of Hunan Province,China(Grant Nos.2019JJ40406,2015JJ4077)the National Natural Science Foundation of China(Nos.61872389,61502540).
文摘The television ratings provide an effective way to analyze the popularity of TV programs and audiences’watching habits.Most previous studies have analyzed the ratings from a single perspective.Few efforts have integrated analysis from different perspectives and explored the reasons for changes in ratings.In this paper,we design a visual analysis system called TVseer to analyze audience ratings from three perspectives:TV channels,TV programs,and audiences.The system can help users explore the factors that affect ratings,and assist them in decisions about program productions and schedules.There are six linked views in TVseer:the channel ratings view and program ratings view show ratings change information from the perspective of TV channels and programs respectively;the overlapping program competition view and the same-type program competition view indicate the competitive relationships among programs;the audience transfer view shows how audiences are moving among different channels;the audience group view displays audience groups based on their watching behavior.Besides,we also construct case studies and expert interviews to prove our system is useful and effective.
文摘To effectively track the impact of population migration between regions on the spread of infectious diseases, this paper proposes a visualized analysis and prediction system of infectious diseases based on the improved SIR model. The research contents including: using the multi graph link interaction mode, visualizing the space-time distribution and development trend of infectious diseases;The LightGBM model is used to track the changes of infection rate and recovery rate, and the Mi/Mo SIR model is constructed according to the initial data of different populations;Mi/Mo SIR model is used to predict infectious diseases in combination with visual panel, providing users with tools to analyze and explain the space-time characteristics and potential laws of infectious diseases. The study found that the closure of cities and the restriction of personnel mobility were necessary and effective, and the system provided an important basis for the prediction and early warning of infectious diseases.
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.
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supported in part by the National Key Research and Development Plan Project(2022YFB3304700)in part by the Xinliao Talent Program of Liaoning Province(XLYC2202002).
文摘As industrial production progresses toward digitalization,massive amounts of data have been collected,transmitted,and stored,with characteristics of large-scale,high-dimensional,heterogeneous,and spatiotemporal dynamics.The high complexity of industrial big data poses challenges for the practical decision-making of domain experts,leading to ever-increasing needs for integrating computational intelligence with human perception into traditional data analysis.Industrial big data visualization integrates theoretical methods and practical technologies from multiple disciplines,including data mining,information visualization,computer graphics,and human-computer interaction,providing a highly effective manner for understanding and exploring the complex industrial processes.This review summarizes the state-of-the-art approaches,characterizes them with six visualization methods,and categorizes them based on analytical tasks and applications.Furthermore,key research challenges and potential future directions are identified.
基金supported by the National Natural Science Fundation of China(61103081)
文摘While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases： the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.
基金the Public Projects of Zhejiang Province,China(Nos.2016C33110,2015C33067)National Natural Science Foundations of China(Nos.61602141,61473108,61402141)
文摘Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.
基金supported by the National Natural Science Foundation of China,Nos.61802128 and 62072183the Science and Technology Commission of Shanghai Municipality,China,No.23002400400.
文摘The regional industry network(RIN)is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region.For most investors and financial analysts lacking extensive experience,the decision-support information provided by industry networks may be too vague.Conversely,RINs express more detailed and specific industry connections both within and out-side the region.As RIN analysis is domain-specific and current financial network analysis tools are designed for gener-alized analytical tasks and cannot be directly applied to RINs,new visual analysis approaches are needed to enhance information exploration efficiency.In this study,we collaborated with domain experts and proposed V4RIN,an inter-active visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data.Through multiple views in the system panel,users can comprehensively explore the structure,geographical distribution,and spatiotemporal variations of the RIN.Two case studies were con-ducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.
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supported by the National Natural Science Foundation of China(62132017,62421003)the Zhejiang Provincial Natural Science Foundation of China(LD24F020011)“Pioneer”and“Leading Goose”R&D Program of Zhejiang(2024C01167).
文摘Mutual funds are one of the most important and popular investment ways for ordinary investors to maintain and increase the value of their assets.However,it is challenging for ordinary investors to select optimal mutual funds from thousands of fund choices managed by different managers.Various investors often have different personal investment preferences and it is difficult to characterize their preferences quickly.Also,mutual fund performance relies on various factors(e.g.,the economic market and the management of fund managers),and most of these factors are dynamically changing,making it difficult to efficiently compare different mutual funds in detail.To address these challenges,we propose FundSelector,an interactive multi-view visual analytics system that quantifies user preferences to rank mutual funds and allows ordinary investors to explore mutual fund performance in terms of multiple factors and scales.Two novel visual designs are proposed to enable detailed comparisons of mutual funds.Rank-informed bipartite contribution bar chart provides interpretable fund ranking results by explicitly showing both positive and negative factors.Elastic trend chart allows investors to analyze and compare the temporal evolution of the mutual funds’performances in a customizable way.We evaluated FundSelector through two case studies and interviews with eight ordinary investors.The results highlight its effectiveness and utility.
基金funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101073876(Ceasefire).
文摘Visual analytics focuses on amplifying users’reasoning and understanding by enhancing data analysis procedures with the efficient incorporation of information visualization and data processing techniques.In this study,we conduct an overview of this multidisciplinary field,focusing on both the process that formalizes its primary concepts and the affiliated research areas.We identify key developments in each area,as well as the challenges that arise when these areas are interconnected under the visual analytics process.We consider that to address the identified challenges,an appropriate representation of key user needs is essential.Therefore,inspired by human-centered design and its principles,we propose a novel methodological approach comprising a human-centered definition of visual analytics that expands on models of the field and quantifies the intermediate states of a data analysis.In addition to the theoretical aspects of the definition,we also provide a set of directions that align the process with technical aspects of the development cycle.In this respect,our research endeavor aims to transform the visual analytics process into an essential method for both conceptualizing data analysis systems capable of anticipating user needs and for streamlining their technical implementation.
文摘Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.
基金the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDB38030300.
文摘With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research field,researchers often hope to quickly learn what is being studied in this field,which research points are receiving high attention,which researchers are studying these research points,and then consider the possibility of collaborating with core researchers on these research points.In addition,students who are preparing for academic further education usually conduct research on mentors and mentors’research platforms,including academic connections,employment opportunities,etc.In order to satisfy these requirements,we(1)design a research point state map based on a science map to help researchers and students understand the development state of a new research field;(2)design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors;(3)designs citation pattern histogram to quickly discover research achievements with high research value,such as the Sleeping Beauty papers,recently hot papers,classic papers and so on.Finally,an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data,and its effectiveness is verified through case studies.
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.
