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

C. M. Albrecht,N. Bobroff, B. Elmegreen,M. Freitag,H. F. Hamann, I. Khabibrakhmanov,L. Klein,S. Lu,F. Marianno,J. Schmude,X. Shao,C. Siebenschuh, R. Zhang

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)(2020)

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摘要
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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关键词
big data analytics,ML,AI,distributed geo-spatial data structures,Hadoop,HBase,Spark,GeoMesa,PAIRS Geoscope
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