Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains.Visualization has proven to be well-suited in supporting t...Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains.Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception.However,non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them.To support novices in learning how to use new digital technologies,the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain.However,empirical evidence on the effectiveness of such approaches is scarce.Therefore,we conducted three studies with Amazon Mechanical Turk(MTurk)workers and students investigating visualization onboarding at different levels:(1)Firstly,we explored the effect of visualization onboarding,using an interactive step-by-step guide,on user performance for four increasingly complex visualization techniques with time-oriented data:a bar chart,a horizon graph,a change matrix,and a parallel coordinates plot.We performed a between-subject experiment with 596 participants in total.The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding.Particularly,participants commented that for highly familiar visualization types no onboarding is needed.However,for the most unfamiliar visualization type—the parallel coordinates plot—performance improvement can be observed with onboarding.(2)Thus,we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types:step-by-step,scrollytelling tutorial,and video tutorial.The study revealed that the video tutorial was ranked as the most positive on average,based on a sentiment analysis,followed by the scrollytelling tutorial and the interactive step-by-step guide.(3)As videos are a traditional method to support users,we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail.Therefore,for our third study,we gathered data towards users’experience in using the in-situ scrollytelling for the VA tool Netflower.The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page.Moreover,for all three studies we explored the effect of task difficulty.In summary,the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool.Additionally,a video tutorial can help to introduce interaction techniques of visualization.展开更多
Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visua...Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.展开更多
基金This work was funded by the Austrian Ministry for Transport,Innovation and Technology(BMVIT)under the ICT of the Future program via the SEVA project(no.874018),as well as the FFG,Contract No.881844:‘‘Pro2Future is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry for Climate Action,Environment,Energy,Mobility,Innovation and Technology,the Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria.COMET is managed by the Austrian Research Promotion Agency FFG’’.
文摘Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains.Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception.However,non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them.To support novices in learning how to use new digital technologies,the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain.However,empirical evidence on the effectiveness of such approaches is scarce.Therefore,we conducted three studies with Amazon Mechanical Turk(MTurk)workers and students investigating visualization onboarding at different levels:(1)Firstly,we explored the effect of visualization onboarding,using an interactive step-by-step guide,on user performance for four increasingly complex visualization techniques with time-oriented data:a bar chart,a horizon graph,a change matrix,and a parallel coordinates plot.We performed a between-subject experiment with 596 participants in total.The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding.Particularly,participants commented that for highly familiar visualization types no onboarding is needed.However,for the most unfamiliar visualization type—the parallel coordinates plot—performance improvement can be observed with onboarding.(2)Thus,we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types:step-by-step,scrollytelling tutorial,and video tutorial.The study revealed that the video tutorial was ranked as the most positive on average,based on a sentiment analysis,followed by the scrollytelling tutorial and the interactive step-by-step guide.(3)As videos are a traditional method to support users,we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail.Therefore,for our third study,we gathered data towards users’experience in using the in-situ scrollytelling for the VA tool Netflower.The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page.Moreover,for all three studies we explored the effect of task difficulty.In summary,the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool.Additionally,a video tutorial can help to introduce interaction techniques of visualization.
基金The authors gratefully acknowledge that this work is a result of the Dagstuhl Seminar 19192 on Visual Analytics for Sets over Time and Space(Fabrikant et al.,2019)Dagstuhl seminars are funded by the Leibniz Association,Germany.Sara Irina Fabrikant gratefully acknowledges funding from the European Research Council(ERC),under the GeoViSense Project,Grant number 740426.
文摘Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
