{"title":"潜水","authors":"Kevin Hu, Diana Orghian, César Hidalgo","doi":"10.1145/3209900.3209910","DOIUrl":null,"url":null,"abstract":"Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.","PeriodicalId":92279,"journal":{"name":"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"DIVE\",\"authors\":\"Kevin Hu, Diana Orghian, César Hidalgo\",\"doi\":\"10.1145/3209900.3209910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.\",\"PeriodicalId\":92279,\"journal\":{\"name\":\"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3209900.3209910\",\"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 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3209900.3209910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generating knowledge from data is an increasingly important activity. This process of data exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and storytelling. Though these tasks are complementary, analysts often execute them in separate tools. Moreover, these tools have steep learning curves due to their reliance on manual query specification. Here, we describe the design and implementation of DIVE, a web-based system that integrates state-of-the-art data exploration features into a single tool. DIVE contributes a mixed-initiative interaction scheme that combines recommendation with point-and-click manual specification, and a consistent visual language that unifies different stages of the data exploration workflow. In a controlled user study with 67 professional data scientists, we find that DIVE users were significantly more successful and faster than Excel users at completing predefined data visualization and analysis tasks.