{"title":"面向协作实验室有效数据选择的质量模型","authors":"Yogesh L. Simmhan, Beth Plale, Dennis Gannon","doi":"10.1109/ICDEW.2006.150","DOIUrl":null,"url":null,"abstract":"Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"Towards a Quality Model for Effective Data Selection in Collaboratories\",\"authors\":\"Yogesh L. Simmhan, Beth Plale, Dennis Gannon\",\"doi\":\"10.1109/ICDEW.2006.150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.\",\"PeriodicalId\":331953,\"journal\":{\"name\":\"22nd International Conference on Data Engineering Workshops (ICDEW'06)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference on Data Engineering Workshops (ICDEW'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDEW.2006.150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2006.150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a Quality Model for Effective Data Selection in Collaboratories
Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.