大型无线电阵列面临的大数据挑战

D. Jones, K. Wagstaff, D. Thompson, L. D'Addario, R. Navarro, C. Mattmann, W. Majid, J. Lazio, R. Preston, U. Rebbapragada
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引用次数: 20

摘要

未来的大型射电天文阵列,特别是平方公里阵列(SKA),将能够以远高于当前实践可承受的分析或存储速率的速度生成数据。根据定义,这是一个“大数据”问题,如果未来的无线电阵列要充分发挥其科学潜力,就需要一个端到端的解决方案。类似的数据处理、传输、存储和管理挑战也面临着许多其他领域的下一代设施。喷气推进实验室正在开发解决大数据问题的技术,重点放在三个领域:1)低功耗数字处理架构,使大容量数据生成在操作上负担得起;2)用于大数据量实时分析(或“数据分类”)的日期自适应机器学习算法;3)可扩展的数据归档系统,允许高效的数据挖掘和远程用户代码在数据存储的本地运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big data challenges for large radio arrays
Future large radio astronomy arrays, particularly the Square Kilometre Array (SKA), will be able to generate data at rates far higher than can be analyzed or stored affordably with current practices. This is, by definition, a "big data" problem, and requires an end-to-end solution if future radio arrays are to reach their full scientific potential. Similar data processing, transport, storage, and management challenges face next-generation facilities in many other fields. The Jet Propulsion Laboratory is developing technologies to address big data issues, with an emphasis in three areas: 1) Lower-power digital processing architectures to make highvolume data generation operationally affordable, 2) Date-adaptive machine learning algorithms for real-time analysis (or "data triage") of large data volumes, and 3) Scalable data archive systems that allow efficient data mining and remote user code to run locally where the data are stored.
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