Uncertainty-aware visual analytics: scope, opportunities, and challenges

  • In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.

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Author:Robin G. C. Maack, Gerik Scheuermann, Hans Hagen, Jose Tiberio Hernández Peñaloza, Christina Gillmann
Parent Title (English):The Visual Computer
Publisher:Springer Nature - Springer
Document Type:Article
Language of publication:English
Date of Publication (online):2024/03/28
Year of first Publication:2022
Publishing Institution:Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau
Date of the Publication (Server):2024/03/28
Page Number:22
First Page:6345
Last Page:6366
Faculties / Organisational entities:Kaiserslautern - Fachbereich Informatik
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Zweitveröffentlichung