Pairs (Re)Loaded: System Design & Benchmarking For Scalable Geospatial Applications

C. Albrecht, N. Bobroff, B. Elmegreen, Marcus Freitag, H. Hamann, Ildar Khabibrakhmanov, Klein Levente, Siyuan Lu, F. Marianno, J. Schmude, X. Shao, Carlo Siebenschuh, Rui Zhang
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引用次数: 3

Abstract

In this paper we benchmark a previously introduced big data platform that enables the analysis of big data from remote sensing and other geospatial-temporal data. The platform, called IBM PAIRS Geoscope, has been developed by leveraging open source big data technologies (Hadoop/HBase) that are in principle scalable in storage and compute to hundreds of PetaBytes. Currently, PAIRS hosts multiple PetaBytes of curated and geospatial-temporally indexed data. It organizes all data with key-value combinations, performing analytics close to the data to minimize data movement.
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成对(重)加载:可扩展地理空间应用的系统设计和基准测试
在本文中,我们对先前介绍的一个大数据平台进行了基准测试,该平台可以分析来自遥感和其他地理时空数据的大数据。这个名为IBM PAIRS Geoscope的平台是利用开源大数据技术(Hadoop/HBase)开发的,原则上可以在存储和计算上扩展到数百pb。目前,pair托管了多个pb的策划和地理时空索引数据。它使用键值组合组织所有数据,在数据附近执行分析,以最大限度地减少数据移动。
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