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Titlebook: Big Scientific Data Management; First International Jianhui Li,Xiaofeng Meng,Zhihui Du Conference proceedings 2019 Springer Nature Switzer

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發(fā)表于 2025-3-21 18:51:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Scientific Data Management
期刊簡(jiǎn)稱First International
影響因子2023Jianhui Li,Xiaofeng Meng,Zhihui Du
視頻videohttp://file.papertrans.cn/186/185749/185749.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Big Scientific Data Management; First International  Jianhui Li,Xiaofeng Meng,Zhihui Du Conference proceedings 2019 Springer Nature Switzer
影響因子.This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018...The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies..
Pindex Conference proceedings 2019
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書(shū)目名稱Big Scientific Data Management影響因子(影響力)




書(shū)目名稱Big Scientific Data Management影響因子(影響力)學(xué)科排名




書(shū)目名稱Big Scientific Data Management網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Big Scientific Data Management網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Big Scientific Data Management被引頻次




書(shū)目名稱Big Scientific Data Management被引頻次學(xué)科排名




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書(shū)目名稱Big Scientific Data Management年度引用學(xué)科排名




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書(shū)目名稱Big Scientific Data Management讀者反饋學(xué)科排名




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An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data,nthetic data with raw files and HBase entries as data sources and result formats, reduced the analyze cost for scientists not familiar with parallel programming while needs to handle a mass of data. We integrate time series anomaly detection algorithms with our parallel dispatching module to achieve
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Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems,ning algorithm model to predict the evolution trend of data access heat. This paper discussed the implementation of some initial parts of the system. Then some preliminary experiments are conducted with these parts.
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Using Hadoop for High Energy Physics Data Analysis,the local file system on data nodes instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop diverse MapReduce model for HEP jobs such as Corsika simulation, ARGO detector simulation and Medea++?reconstruction. And we develop a toolkit for users t
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Multi-dimensional Index over a Key-Value Store for Semi-structured Data, unit of the range partitioned Key-value store) to utilize distributed computing and data locality. Our prototype of MD-Index is built on HBase, the standard Key-value database. Experimental results reveal that MD-Index is capable of storing and retrieving trillions of semi-structured data and achie
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