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Application of Cloud Computing for Big Data in the X-Ray Crystallography Community

Published: 07 March 2020 Publication History

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.

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ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management
January 2020
258 pages
ISBN:9781450376907
DOI:10.1145/3378936
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Science and Technology of China: University of Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2020

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Author Tags

  1. Big Data
  2. Cloud computing
  3. Crystallography
  4. In-Memory warehousing

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