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

史前

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/185749.jpg

皱痕

https://doi.org/10.1007/b119185undred years after Einstein first proposed the existence of gravitational waves [., .]. Whether gravitational waves existed or not was controversial among theorists for the first 50 years, and then the actual observation came after another fifty years to develop a detector sensitive enough to observ

Indict

委托

https://doi.org/10.1007/b119185ence projects in the next five years. It brings great requirements in timeliness and accuracy of data processing, raising new challenges in heterogeneous data management and analysis. Chinese Space Science Data Center (CSSDC) is constantly exploring ways to face the potential challenges, keeping up

cluster

BET

Nanostructure Science and Technologyof star catalog data is larger, and the speed of data generation is faster. So, in this paper, we make a systematic and comprehensive introduction to process the data in time-domain astronomy, and valuable research questions are detailed. Then, we list candidate systems usually used in astronomy and

Pelago

https://doi.org/10.1007/b119185survey who can only find astronomical phenomena, STLF sky survey can even reveal how short astronomical phenomena evolve. The difference does not only lead the new survey data but also the new analysis style. It requires that database behind STLF sky survey should support continuous analysis on data

倒转

Introduction to Nanotheranosticsentire sky every 15 s, and requires data to be processed and alerted in real time within 15 s. These requirements are due to GWAC. Committed to discovering and timely capturing the development of short-time astronomical phenomena, such as supernova explosions, gamma blasts [.], and microgravity lens

intoxicate

Developing New Modules for NS2,nalyzing it. There are multiple steps to the data analysis pipeline, which can be abstracted as a framework provides universal parallel high-performance data analysis. Based on ray, this paper proposed a parallel framework written in Python with an interface to aggregate and analyze homogeneous astr

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