.png){kind=icon}
返回
Distributed asteroid discovery system for large astronomical data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jnca.2017.03.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Thanks to the advanced technology in astronomical observations, astronomers have collected massive data sets. One of the research tasks is to discover asteroids based on observational data. However, with the fast-growing volume of astronomical data sets, this task becomes enormously relied on computing power. Therefore, the cloud-enable distributed computation and super powerful computation power can offer a good solution to this field. We adopt the Hough transform to link the sequential DETECTIONs of asteroids, and also design a distributed algorithm to process this task on distributed cloud environment for flexible and efficient calculation. Our designs are developed on MapReduce and Spark frameworks. We also utilizes HDFS and HBase in order to reduce disk I/O overhead and increase reliability and scalability. The system architecture is built on the OpenStack which allocates hardware resources flexibly. Our experiment results show significant improvements of calculation time. We believe this will help astronomers to interactively access and analyze data to discover asteroids. The system can also incrementally update DETECTIONs linking with new observed data. It also provides the visual interface to inspect the linked DETECTIONs of asteroids.
Keyword:
Big data
Cloud computing
Distributed system
Asteroid track
Hough transform algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8
论文数:
3.7K
被引数:
1.1W
机构
引用论文
Molecular mapping of the Cromer blood group Cra and Tca epitopes of decay accelerating factor: toward the use of recombinant antigens in immunohematology.png)
Blood
OAAI
BloodIF0
OAAI分享收藏
查看更多内容