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dc.contributor.authorAfzal, Shehzad
dc.contributor.authorHittawe, M. M.
dc.contributor.authorGhani, Sohaib
dc.contributor.authorJamil, Tahira
dc.contributor.authorKnio, Omar
dc.contributor.authorHadwiger, Markus
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2019-09-17T13:20:43Z
dc.date.available2019-09-17T13:20:43Z
dc.date.issued2019-07-10
dc.identifier.citationAfzal, S., Hittawe, M. M., Ghani, S., Jamil, T., Knio, O., Hadwiger, M., & Hoteit, I. (2019). The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets. Computer Graphics Forum, 38(3), 881–907. doi:10.1111/cgf.13731
dc.identifier.doi10.1111/cgf.13731
dc.identifier.urihttp://hdl.handle.net/10754/656777
dc.description.abstractThe analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense-making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012).
dc.description.sponsorshipThis study was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the “Virtual Red Sea Initiative” (award #REP/1/3268-01-01). We want to acknowledge Dr. Hari Prasad Dasari for his help and contribution in organizing the interviews with domain experts. We also like to thank KAUST Visualization Core Lab for their help and support.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13731
dc.rightsArchived with thanks to Computer Graphics Forum
dc.subjectCCS Concepts
dc.subject• Human Centered Computing → Visualization
dc.subjectVisual Analytics
dc.subject• Physical Sciences and Engineering → Earth and atmospheric sciences
dc.titleThe state of the art in visual analysis approaches for ocean and atmospheric datasets
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentVisualization
dc.identifier.journalComputer Graphics Forum
dc.rights.embargodate2020-01-01
dc.eprint.versionPost-print
kaust.personAfzal, Shehzad
kaust.personHittawe, M. M.
kaust.personGhani, Sohaib
kaust.personJamil, Tahira
kaust.personKnio, Omar
kaust.personHadwiger, Markus
kaust.personHoteit, Ibrahim
kaust.grant.numberREP/1/3268-01-01
refterms.dateFOA2020-01-01T00:00:00Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)
kaust.acknowledged.supportUnitVisualization Core Lab
dc.date.published-online2019-07-10
dc.date.published-print2019-06


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