{"title":"与可重排序矩阵的相互作用","authors":"H. Siirtola","doi":"10.1109/IV.1999.781570","DOIUrl":null,"url":null,"abstract":"The Reorderable Matrix is a simple visualization method for quantitative tabular data. The paper examines how first-time users interact with the Reorderable Matrix and how well they perform a simple task of finding correlating attributes. Visualizing a set of data is a common task in various activities such as decision making or opinion forming. Typical situations are a person making business related decisions, a doctor examining test results of a patient or an engineer making choices between different constructs. All these situations involve examining complex data interactions in a limited time. In this experiment the participants were interacting with the Reorderable Matrix for the first time and tried to find correlating attributes from an unfamiliar set of data.","PeriodicalId":340240,"journal":{"name":"1999 IEEE International Conference on Information Visualization (Cat. No. PR00210)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Interaction with the Reorderable Matrix\",\"authors\":\"H. Siirtola\",\"doi\":\"10.1109/IV.1999.781570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Reorderable Matrix is a simple visualization method for quantitative tabular data. The paper examines how first-time users interact with the Reorderable Matrix and how well they perform a simple task of finding correlating attributes. Visualizing a set of data is a common task in various activities such as decision making or opinion forming. Typical situations are a person making business related decisions, a doctor examining test results of a patient or an engineer making choices between different constructs. All these situations involve examining complex data interactions in a limited time. In this experiment the participants were interacting with the Reorderable Matrix for the first time and tried to find correlating attributes from an unfamiliar set of data.\",\"PeriodicalId\":340240,\"journal\":{\"name\":\"1999 IEEE International Conference on Information Visualization (Cat. No. PR00210)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1999 IEEE International Conference on Information Visualization (Cat. No. PR00210)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IV.1999.781570\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 IEEE International Conference on Information Visualization (Cat. No. PR00210)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IV.1999.781570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Reorderable Matrix is a simple visualization method for quantitative tabular data. The paper examines how first-time users interact with the Reorderable Matrix and how well they perform a simple task of finding correlating attributes. Visualizing a set of data is a common task in various activities such as decision making or opinion forming. Typical situations are a person making business related decisions, a doctor examining test results of a patient or an engineer making choices between different constructs. All these situations involve examining complex data interactions in a limited time. In this experiment the participants were interacting with the Reorderable Matrix for the first time and tried to find correlating attributes from an unfamiliar set of data.