{"title":"大数据云计算在x射线晶体学界的应用","authors":"A. Tosson, M. Shokr, U. Pietsch","doi":"10.1145/3378936.3378950","DOIUrl":null,"url":null,"abstract":"The X-ray crystallography community has recently been affected by a significant increase in data volume caused by the use of advanced detector technologies and the new generation of high brilliance light sources. The fact that forced the decision makers to implement Big Data analytics, aiming to achieve a suitable environment for scientists at experimental and post-experimental phases. This paper demonstrates an extension of our approach towards a compact platform which provides the scientists with the digital ecosystem for the systematic harvest of data. It introduces an innovative solution to use warehousing and cloud computing to manage datasets collected by 2D energy-dispersive detectors, for an example. Moreover, it suggests that, deploying a Software as a Service (SaaS) cloud model, a public cloud data center, and cloud-based in-memory warehousing architecture, it is possible to dramatically reduce both hardware and processing costs.","PeriodicalId":304149,"journal":{"name":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Cloud Computing for Big Data in the X-Ray Crystallography Community\",\"authors\":\"A. Tosson, M. Shokr, U. Pietsch\",\"doi\":\"10.1145/3378936.3378950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The X-ray crystallography community has recently been affected by a significant increase in data volume caused by the use of advanced detector technologies and the new generation of high brilliance light sources. The fact that forced the decision makers to implement Big Data analytics, aiming to achieve a suitable environment for scientists at experimental and post-experimental phases. This paper demonstrates an extension of our approach towards a compact platform which provides the scientists with the digital ecosystem for the systematic harvest of data. It introduces an innovative solution to use warehousing and cloud computing to manage datasets collected by 2D energy-dispersive detectors, for an example. Moreover, it suggests that, deploying a Software as a Service (SaaS) cloud model, a public cloud data center, and cloud-based in-memory warehousing architecture, it is possible to dramatically reduce both hardware and processing costs.\",\"PeriodicalId\":304149,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Software Engineering and Information Management\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Software Engineering and Information Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3378936.3378950\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Software Engineering and Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3378936.3378950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Cloud Computing for Big Data in the X-Ray Crystallography Community
The X-ray crystallography community has recently been affected by a significant increase in data volume caused by the use of advanced detector technologies and the new generation of high brilliance light sources. The fact that forced the decision makers to implement Big Data analytics, aiming to achieve a suitable environment for scientists at experimental and post-experimental phases. This paper demonstrates an extension of our approach towards a compact platform which provides the scientists with the digital ecosystem for the systematic harvest of data. It introduces an innovative solution to use warehousing and cloud computing to manage datasets collected by 2D energy-dispersive detectors, for an example. Moreover, it suggests that, deploying a Software as a Service (SaaS) cloud model, a public cloud data center, and cloud-based in-memory warehousing architecture, it is possible to dramatically reduce both hardware and processing costs.