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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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21#
發(fā)表于 2025-3-25 05:17:57 | 只看該作者
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發(fā)表于 2025-3-25 21:24:55 | 只看該作者
Data Management in Time-Domain Astronomy: Requirements and Challenges,of 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
26#
發(fā)表于 2025-3-26 01:18:47 | 只看該作者
AstroServ: A Distributed Database for Serving Large-Scale Full Life-Cycle Astronomical Data,survey 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
27#
發(fā)表于 2025-3-26 08:14:44 | 只看該作者
28#
發(fā)表于 2025-3-26 10:41:23 | 只看該作者
An Efficient Parallel Framework to Analyze Astronomical Sky Survey Data,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
29#
發(fā)表于 2025-3-26 14:48:15 | 只看該作者
Real-Time Query Enabled by Variable Precision in Astronomy,ially, in time-domain astronomy, Short-Timescale and Large Field-of-view (STLF) sky survey not only requires real-time analysis on short-time data, but also need precise astronomical data for special phenomena. Additionally, it is important to find a partition method and build an index based on that
30#
發(fā)表于 2025-3-26 18:13:38 | 只看該作者
Continuous Cross Identification in Large-Scale Dynamic Astronomical Data Flow,ation. Furthermore, transient survey projects are required to select the candidates fast from large volume data. However, traditional cross identification methods didn’t satisfy the observation of transient survey. We present a fast and efficient cross identification system for large-scale astronomi
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