{"title":"HeloVis: A Helical Visualization for SIGINT Analysis Using 3D Immersion","authors":"Alma Cantu, Thierry Duval, O. Grisvard, G. Coppin","doi":"10.1109/PacificVis.2018.00030","DOIUrl":null,"url":null,"abstract":"In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIGINT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.","PeriodicalId":164616,"journal":{"name":"2018 IEEE Pacific Visualization Symposium (PacificVis)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Pacific Visualization Symposium (PacificVis)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PacificVis.2018.00030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIGINT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.