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
{"title":"Pairs (Re)Loaded: System Design & Benchmarking For Scalable Geospatial Applications","authors":"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","doi":"10.1109/LAGIRS48042.2020.9165675","DOIUrl":null,"url":null,"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.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LAGIRS48042.2020.9165675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信