{"title":"大数据分析系统的场景驱动架构评估方法","authors":"Edmon Begoli, Theodore F. Chila, W. Inmon","doi":"10.1109/SysCon.2013.6549857","DOIUrl":null,"url":null,"abstract":"The methodology we present in this paper emerged as a result of the technical and organizational assessment we conducted for a large data analytic system and for its expansion to support a significant new mission in healthcare domain. We developed a 4+1 dimensional approach for examining the different characteristics of a system with four traditional dimensions and a fifth, scenarios-based, dimension, introduced as an exploration device of the entire system in its business context. We present the principles, guidelines, and structure of the methodology as well as the results of the application of this process leading to a credible evaluation that better assesses current large data analysis systems than the previous, purely static assessment.","PeriodicalId":218073,"journal":{"name":"2013 IEEE International Systems Conference (SysCon)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Scenario-driven architecture assessment methodology for large data analysis systems\",\"authors\":\"Edmon Begoli, Theodore F. Chila, W. Inmon\",\"doi\":\"10.1109/SysCon.2013.6549857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The methodology we present in this paper emerged as a result of the technical and organizational assessment we conducted for a large data analytic system and for its expansion to support a significant new mission in healthcare domain. We developed a 4+1 dimensional approach for examining the different characteristics of a system with four traditional dimensions and a fifth, scenarios-based, dimension, introduced as an exploration device of the entire system in its business context. We present the principles, guidelines, and structure of the methodology as well as the results of the application of this process leading to a credible evaluation that better assesses current large data analysis systems than the previous, purely static assessment.\",\"PeriodicalId\":218073,\"journal\":{\"name\":\"2013 IEEE International Systems Conference (SysCon)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Systems Conference (SysCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SysCon.2013.6549857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Systems Conference (SysCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SysCon.2013.6549857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Scenario-driven architecture assessment methodology for large data analysis systems
The methodology we present in this paper emerged as a result of the technical and organizational assessment we conducted for a large data analytic system and for its expansion to support a significant new mission in healthcare domain. We developed a 4+1 dimensional approach for examining the different characteristics of a system with four traditional dimensions and a fifth, scenarios-based, dimension, introduced as an exploration device of the entire system in its business context. We present the principles, guidelines, and structure of the methodology as well as the results of the application of this process leading to a credible evaluation that better assesses current large data analysis systems than the previous, purely static assessment.