{"title":"环境传感器数据的可视化统计分析","authors":"Bindu Gupta, Kaushal Paneri, Gunjan Sehgal, Karamjit Singh, Geetika Sharma, Gautam M. Shroff","doi":"10.1109/VAST.2017.8585515","DOIUrl":null,"url":null,"abstract":"We attempted the VAST MC2 challenge following a statistical modelling approach along with interactive visualizations to analyse and extract insights from the data. We use Bayesian networks to model dependencies between given and derived data attributes along with visual analytics techniques to answer the questions posed by the challenge.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visual Statistical Analysis of Environmental Sensor Data\",\"authors\":\"Bindu Gupta, Kaushal Paneri, Gunjan Sehgal, Karamjit Singh, Geetika Sharma, Gautam M. Shroff\",\"doi\":\"10.1109/VAST.2017.8585515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We attempted the VAST MC2 challenge following a statistical modelling approach along with interactive visualizations to analyse and extract insights from the data. We use Bayesian networks to model dependencies between given and derived data attributes along with visual analytics techniques to answer the questions posed by the challenge.\",\"PeriodicalId\":149607,\"journal\":{\"name\":\"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)\",\"volume\":\"247 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VAST.2017.8585515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VAST.2017.8585515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual Statistical Analysis of Environmental Sensor Data
We attempted the VAST MC2 challenge following a statistical modelling approach along with interactive visualizations to analyse and extract insights from the data. We use Bayesian networks to model dependencies between given and derived data attributes along with visual analytics techniques to answer the questions posed by the challenge.