{"title":"应用ATML测试结果和内部存储，方便智能数据分析","authors":"A. Smith, H. Wanigaratne","doi":"10.1109/AUTEST.2012.6334520","DOIUrl":null,"url":null,"abstract":"Many organizations today struggle with getting meaningful insights out of their Test Data. Partly this is due to the complexity of the collection and aggregation of the data, and also partly due to the actual types of data that is recorded at the Test Stations. With some careful planning, the Test Data can be full of rich insights if some standard tags are added to the overall Test Data format.","PeriodicalId":142978,"journal":{"name":"2012 IEEE AUTOTESTCON Proceedings","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of ATML test results and intrastage to facilitate intelligent data analysis\",\"authors\":\"A. Smith, H. Wanigaratne\",\"doi\":\"10.1109/AUTEST.2012.6334520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many organizations today struggle with getting meaningful insights out of their Test Data. Partly this is due to the complexity of the collection and aggregation of the data, and also partly due to the actual types of data that is recorded at the Test Stations. With some careful planning, the Test Data can be full of rich insights if some standard tags are added to the overall Test Data format.\",\"PeriodicalId\":142978,\"journal\":{\"name\":\"2012 IEEE AUTOTESTCON Proceedings\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE AUTOTESTCON Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AUTEST.2012.6334520\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE AUTOTESTCON Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AUTEST.2012.6334520","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of ATML test results and intrastage to facilitate intelligent data analysis
Many organizations today struggle with getting meaningful insights out of their Test Data. Partly this is due to the complexity of the collection and aggregation of the data, and also partly due to the actual types of data that is recorded at the Test Stations. With some careful planning, the Test Data can be full of rich insights if some standard tags are added to the overall Test Data format.
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