For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have ...For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks.In both bodies of literature,networks are frequently referred to as being‘large’or‘complex’,yet these terms are relative.From a human-centred,experiment point-of-view,what constitutes‘large’(for example)depends on several factors,such as data complexity,visual complexity,and the technology used.In this paper,we survey the literature on human-centred experiments to understand how,in practice,different features and characteristics of node–link diagrams affect visual complexity.展开更多
This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia(Monash IA Lab).The lab has been influ...This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia(Monash IA Lab).The lab has been influential with contributions in algorithms,interaction techniques and experimental results in Network Visualisation,Interactive Optimisation and Geographic and Cartographic visualisation.It has also been a leader in the emerging topic of Immersive Analytics,which explores natural interactions and immersive display technologies in support of data analytics.We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.展开更多
Eye tracking is growing in popularity for multiple application areas,yet analysing and exploring the large volume of complex data remains difficult for most users.We present a comprehensive eye tracking visual analyti...Eye tracking is growing in popularity for multiple application areas,yet analysing and exploring the large volume of complex data remains difficult for most users.We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner.The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration.The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly.The system was motivated by the need to analyse eye-tracking data collected from an‘in the wild’study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains.Results suggest that,thanks to state-of-the-art visualisation techniques and by providing context with videos,our system could enable an improved analysis of eye-tracking data through interactive exploration,facilitating comparison between different participants or conditions,thus enhancing the presentation of complex data analysis to non-experts.This research paper provides four contributions:(1)analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data;(2)a highly dynamic system to visually explore and present complex eye-tracking data;(3)insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.展开更多
In this paper,we list the goals for and the pros and cons of guidance,and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of vi...In this paper,we list the goals for and the pros and cons of guidance,and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics.Recent advances in artificial intelligence,particularly in machine learning,have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts.However,visual analytics remains a complex activity,combining many different subtasks.Some of these tasks are relatively low-level,and it is clear how automation could play a role—for example,classification and clustering of data.Other tasks are much more abstract and require significant human creativity,for example,linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making.In this paper,we outline the potential applications of guidance,as well as the inputs to guidance.We discuss challenges in implementing guidance,including the inputs to guidance systems and how to provide guidance to users.We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.展开更多
基金This survey began as part of a working group output of the NII Shonan Seminar No.2015-1 Big Graph Drawing:Metrics and Methods,and we would like to thank this seminar series for the role it played in this surveyWe would like to thank Tamara Munzner for her ideas and feedback at this seminar which helped focus the topic of this paper.The second author would like to thank EPSRC First Grant EP/N005724/1+1 种基金The last author would like to thank the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 747985This work was supported by the Australian Research Council Discovery Project grant DP140100077.
文摘For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks.In both bodies of literature,networks are frequently referred to as being‘large’or‘complex’,yet these terms are relative.From a human-centred,experiment point-of-view,what constitutes‘large’(for example)depends on several factors,such as data complexity,visual complexity,and the technology used.In this paper,we survey the literature on human-centred experiments to understand how,in practice,different features and characteristics of node–link diagrams affect visual complexity.
基金This work was supported by the Australian Research Council through grants DP140100077 and DP180100755.
文摘This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia(Monash IA Lab).The lab has been influential with contributions in algorithms,interaction techniques and experimental results in Network Visualisation,Interactive Optimisation and Geographic and Cartographic visualisation.It has also been a leader in the emerging topic of Immersive Analytics,which explores natural interactions and immersive display technologies in support of data analytics.We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.
基金The observational study that motivated the initial develop-ment of VETA was funded by the Australian Energy Market Opera-tor(AEMO).We would like to thank all AEMO participants and all eye tracking experts who participated in the evaluation.We also acknowledge the use of Monash Business Behavioural Laboratory equipment in this project.
文摘Eye tracking is growing in popularity for multiple application areas,yet analysing and exploring the large volume of complex data remains difficult for most users.We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner.The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration.The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly.The system was motivated by the need to analyse eye-tracking data collected from an‘in the wild’study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains.Results suggest that,thanks to state-of-the-art visualisation techniques and by providing context with videos,our system could enable an improved analysis of eye-tracking data through interactive exploration,facilitating comparison between different participants or conditions,thus enhancing the presentation of complex data analysis to non-experts.This research paper provides four contributions:(1)analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data;(2)a highly dynamic system to visually explore and present complex eye-tracking data;(3)insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community.
基金This work was partly supported by the Natural Sciences and Engineering Research Coun-cil of Canada(NSERC)[grant RGPIN-2015-03916],the Fraunhofer Cluster of Excellence on"Cognitive Internet Technologies"by the EU through project Track&Know(grant agreement 780754).
文摘In this paper,we list the goals for and the pros and cons of guidance,and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics.Recent advances in artificial intelligence,particularly in machine learning,have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts.However,visual analytics remains a complex activity,combining many different subtasks.Some of these tasks are relatively low-level,and it is clear how automation could play a role—for example,classification and clustering of data.Other tasks are much more abstract and require significant human creativity,for example,linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making.In this paper,we outline the potential applications of guidance,as well as the inputs to guidance.We discuss challenges in implementing guidance,including the inputs to guidance systems and how to provide guidance to users.We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
