{"title":"科学工作流程推理","authors":"Z. Lacroix, C. Legendre, S. Tüzmen","doi":"10.1109/SERVICES-I.2009.73","DOIUrl":null,"url":null,"abstract":"Scientific workflows describe the scientific process from experimental design, data capture, integration, processing, and analysis that leads to scientific discovery. Laboratory Information Management Systems (LIMS) coordinate the management of wet lab tasks, samples, and instruments and allow reasoning on business-like parameters such as ordering (e.g., invoicing) and organization (automation and optimization) whereas workflow systems support the design of workflows in-silico for their execution. We present an approach that supports reasoning on scientific workflows that mix wet and digital tasks. Indeed, experiments are often first designed and simulated with digital resources in order to predict the quality of the result or to identify the parameters suitable for the expected outcome. ProtocolDB allows the design of scientific workflows that may combine wet and digital tasks and provides the framework for prediction and reasoning on performance, quality, and cost.","PeriodicalId":159235,"journal":{"name":"2009 Congress on Services - I","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Reasoning on Scientific Workflows\",\"authors\":\"Z. Lacroix, C. Legendre, S. Tüzmen\",\"doi\":\"10.1109/SERVICES-I.2009.73\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Scientific workflows describe the scientific process from experimental design, data capture, integration, processing, and analysis that leads to scientific discovery. Laboratory Information Management Systems (LIMS) coordinate the management of wet lab tasks, samples, and instruments and allow reasoning on business-like parameters such as ordering (e.g., invoicing) and organization (automation and optimization) whereas workflow systems support the design of workflows in-silico for their execution. We present an approach that supports reasoning on scientific workflows that mix wet and digital tasks. Indeed, experiments are often first designed and simulated with digital resources in order to predict the quality of the result or to identify the parameters suitable for the expected outcome. ProtocolDB allows the design of scientific workflows that may combine wet and digital tasks and provides the framework for prediction and reasoning on performance, quality, and cost.\",\"PeriodicalId\":159235,\"journal\":{\"name\":\"2009 Congress on Services - I\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Congress on Services - I\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SERVICES-I.2009.73\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Congress on Services - I","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SERVICES-I.2009.73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scientific workflows describe the scientific process from experimental design, data capture, integration, processing, and analysis that leads to scientific discovery. Laboratory Information Management Systems (LIMS) coordinate the management of wet lab tasks, samples, and instruments and allow reasoning on business-like parameters such as ordering (e.g., invoicing) and organization (automation and optimization) whereas workflow systems support the design of workflows in-silico for their execution. We present an approach that supports reasoning on scientific workflows that mix wet and digital tasks. Indeed, experiments are often first designed and simulated with digital resources in order to predict the quality of the result or to identify the parameters suitable for the expected outcome. ProtocolDB allows the design of scientific workflows that may combine wet and digital tasks and provides the framework for prediction and reasoning on performance, quality, and cost.