A Survey on Progressive Visualization

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex comput...

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Vydané v:	IEEE transactions on visualization and computer graphics Ročník 30; číslo 9; s. 6447 - 6467
Hlavní autori:	Ulmer, Alex, Angelini, Marco, Fekete, Jean-Daniel, Kohlhammer, Jorn, May, Thorsten
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2024 Institute of Electrical and Electronics Engineers
Predmet:	Computer Science Convergence Data visualization Human-Computer Interaction Progressive visual analytics progressive visualization Rendering (computer graphics) state-of-the-art report survey Surveys Task analysis Taxonomy Visual analytics State-of-the-Art Report Progressive Visual Analytics Survey Progressive Visualization Taxonomy
ISSN:	1077-2626, 1941-0506, 1941-0506
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Abstract	Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.
AbstractList	Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva. Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges.
Author	Kohlhammer, Jorn Angelini, Marco May, Thorsten Fekete, Jean-Daniel Ulmer, Alex
Author_xml	– sequence: 1 givenname: Alex orcidid: 0009-0008-3778-8184 surname: Ulmer fullname: Ulmer, Alex email: alex.ulmer@igd.fraunhofer.de organization: Fraunhofer IGD, Darmstadt, Germany – sequence: 2 givenname: Marco orcidid: 0000-0001-9051-6972 surname: Angelini fullname: Angelini, Marco email: angelini@dis.uniroma1.it organization: Link Campus University, Rome, Italy – sequence: 3 givenname: Jean-Daniel orcidid: 0000-0003-3770-8726 surname: Fekete fullname: Fekete, Jean-Daniel email: jean-daniel.fekete@inria.fr organization: CNRS, Inria Saclay, Université Paris-Saclay, Gif-sur-Yvette, France – sequence: 4 givenname: Jorn orcidid: 0000-0003-1706-8979 surname: Kohlhammer fullname: Kohlhammer, Jorn email: joern.kohlhammer@igd.fraunhofer.de organization: Fraunhofer IGD, Darmstadt, Germany – sequence: 5 givenname: Thorsten orcidid: 0000-0001-8027-2687 surname: May fullname: May, Thorsten email: thorsten.may@igd.fraunhofer.de organization: Fraunhofer IGD, Darmstadt, Germany
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Snippet	Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization...
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SubjectTerms	Computer Science Convergence Data visualization Human-Computer Interaction Progressive visual analytics progressive visualization Rendering (computer graphics) state-of-the-art report survey Surveys Task analysis Taxonomy Visual analytics
Title	A Survey on Progressive Visualization
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