Efficient astronomical data classification on large-scale distributed systems

Cheng Hsien Tang, Min Feng Wang, Wei Jen Wang, Meng Feng Tsai, Yuji Urata, Chow Choong Ngeow, Induk Lee, Kuiyun Huang, Wen Ping Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Classification of different kinds of space objects plays an important role in many astronomy areas. Nowadays the classification process can possibly involve a huge amount of data. It could take a long time for processing and demand many resources for computation and storage. In addition, it may also take much effort to train a qualified expert who needs to have both the astronomy domain knowledge and the capability to manipulate the data. This research intends to provide an efficient, scalable classification system for astronomy research. We implement a dynamic classification framework and system using support vector machines (SVMs). The proposed system is based on a large-scale, distributed storage environment, on which scientists can design their analysis processes in a more abstract manner, instead of an awkward and time-consuming approach which searches and collects related subset of data from the huge data set. The experimental results confirm that our system is scalable and efficient.

Original languageEnglish
Title of host publicationAdvances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings
Number of pages11
StatePublished - 2010
Event5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010 - Hualien, Taiwan
Duration: 10 May 201013 May 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6104 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010


  • Classification
  • Data Center
  • Distributed System
  • Support Vector Machine


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