{"title":"可视化降维数据:与分析师的访谈和任务序列的表征","authors":"M. Brehmer, M. Sedlmair, S. Ingram, T. Munzner","doi":"10.1145/2669557.2669559","DOIUrl":null,"url":null,"abstract":"We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.","PeriodicalId":179584,"journal":{"name":"Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"85","resultStr":"{\"title\":\"Visualizing dimensionally-reduced data: interviews with analysts and a characterization of task sequences\",\"authors\":\"M. Brehmer, M. Sedlmair, S. Ingram, T. Munzner\",\"doi\":\"10.1145/2669557.2669559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.\",\"PeriodicalId\":179584,\"journal\":{\"name\":\"Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"85\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2669557.2669559\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2669557.2669559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visualizing dimensionally-reduced data: interviews with analysts and a characterization of task sequences
We characterize five task sequences related to visualizing dimensionally-reduced data, drawing from data collected from interviews with ten data analysts spanning six application domains, and from our understanding of the technique literature. Our characterization of visualization task sequences for dimensionally-reduced data fills a gap created by the abundance of proposed techniques and tools that combine high-dimensional data analysis, dimensionality reduction, and visualization, and is intended to be used in the design and evaluation of future techniques and tools. We discuss implications for the evaluation of existing work practices, for the design of controlled experiments, and for the analysis of post-deployment field observations.