@inproceedings{499ab540ea6d4ea3abe3484c8657ad5e,
title = "An efficient astronomical cross-matching model based on MapReduce mechanism",
abstract = "In order to perform an effective cross-matching computation on an enormous amount of text-file-based astronomical observation data, this study proposes an algorithm based on the MapReduce distributed architecture. Such an approach not only greatly enhances the computation speed, but also provides a data structure for storing the computation results. It provides a satisfactory solution not only for cross-matching the entirety of the data, but also for simply updating the changes.",
keywords = "Astronomical cross-matching, Big data science, Computational modeling, Distributed computing, MapReduce",
author = "Lee, {Kuei Sheng} and Tsai, {Meng Feng} and Yuji Urata and Huang, {Kui Yun} and Huang, {Chi Sheng}",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; ASE BigData and SocialInformatics, ASE BD and SI 2015 ; Conference date: 07-10-2015 Through 09-10-2015",
year = "2015",
month = oct,
day = "7",
doi = "10.1145/2818869.2818888",
language = "???core.languages.en_GB???",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015",
}