{"title":"从数据裁剪、分析算法和可视化中生成洞察力,以满足用户需求","authors":"Paul D. Baxter, T. Wood","doi":"10.1109/CyberSA.2016.7503284","DOIUrl":null,"url":null,"abstract":"There are many different tools available for web analytics for business intelligence and empowerment. To be useful for a user community, data analytics requires ascertaining the users' needs to drive a combination of appropriate analytical algorithms and effective visualization. Should any of these three be missing or tackled without regard for the others, data analysis will be carried out without enabling the users to move from data to action. Using the example of the Transport for London (TfL) open data set on tube journeys we provide two examples of the combination of algorithms, visualization and user requirements, one of which is described in detail here, while the other is described at a summary level.","PeriodicalId":179031,"journal":{"name":"2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generating insight from data tailoring analytic algorithms and visualization to address user requirements\",\"authors\":\"Paul D. Baxter, T. Wood\",\"doi\":\"10.1109/CyberSA.2016.7503284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are many different tools available for web analytics for business intelligence and empowerment. To be useful for a user community, data analytics requires ascertaining the users' needs to drive a combination of appropriate analytical algorithms and effective visualization. Should any of these three be missing or tackled without regard for the others, data analysis will be carried out without enabling the users to move from data to action. Using the example of the Transport for London (TfL) open data set on tube journeys we provide two examples of the combination of algorithms, visualization and user requirements, one of which is described in detail here, while the other is described at a summary level.\",\"PeriodicalId\":179031,\"journal\":{\"name\":\"2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CyberSA.2016.7503284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CyberSA.2016.7503284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generating insight from data tailoring analytic algorithms and visualization to address user requirements
There are many different tools available for web analytics for business intelligence and empowerment. To be useful for a user community, data analytics requires ascertaining the users' needs to drive a combination of appropriate analytical algorithms and effective visualization. Should any of these three be missing or tackled without regard for the others, data analysis will be carried out without enabling the users to move from data to action. Using the example of the Transport for London (TfL) open data set on tube journeys we provide two examples of the combination of algorithms, visualization and user requirements, one of which is described in detail here, while the other is described at a summary level.