Nuno Cid Martins, Bernardo Marques, Paulo Dias, B. Sousa Santos
{"title":"Expanding the Horizons of Situated Visualization: The Extended SV Model","authors":"Nuno Cid Martins, Bernardo Marques, Paulo Dias, B. Sousa Santos","doi":"10.3390/bdcc7020112","DOIUrl":null,"url":null,"abstract":"To fully leverage the benefits of augmented and mixed reality (AR/MR) in supporting users, it is crucial to establish a consistent and well-defined situated visualization (SV) model. SV encompasses visualizations that adapt based on context, considering the relevant visualizations within their physical display environment. Recognizing the potential of SV in various domains such as collaborative tasks, situational awareness, decision-making, assistance, training, and maintenance, AR/MR is well-suited to facilitate these scenarios by providing additional data and context-driven visualization techniques. While some perspectives on the SV model have been proposed, such as space, time, place, activity, and community, a comprehensive and up-to-date systematization of the entire SV model is yet to be established. Therefore, there is a pressing need for a more comprehensive and updated description of the SV model within the AR/MR framework to foster research discussions.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/bdcc7020112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
To fully leverage the benefits of augmented and mixed reality (AR/MR) in supporting users, it is crucial to establish a consistent and well-defined situated visualization (SV) model. SV encompasses visualizations that adapt based on context, considering the relevant visualizations within their physical display environment. Recognizing the potential of SV in various domains such as collaborative tasks, situational awareness, decision-making, assistance, training, and maintenance, AR/MR is well-suited to facilitate these scenarios by providing additional data and context-driven visualization techniques. While some perspectives on the SV model have been proposed, such as space, time, place, activity, and community, a comprehensive and up-to-date systematization of the entire SV model is yet to be established. Therefore, there is a pressing need for a more comprehensive and updated description of the SV model within the AR/MR framework to foster research discussions.