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

doi:10.1007/978-3-642-13067-0_45

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 proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 language	English
Title of host publication	Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings
Pages	430-440
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-13067-0_45
State	Published - 2010
Event	5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010 - Hualien, Taiwan Duration: 10 May 2010 → 13 May 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6104 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010
Country/Territory	Taiwan
City	Hualien
Period	10/05/10 → 13/05/10

Keywords

Classification
Data Center
Distributed System
Support Vector Machine

Access to Document

10.1007/978-3-642-13067-0_45

Cite this

Tang, C. H., Wang, M. F., Wang, W. J., Tsai, M. F., Urata, Y., Ngeow, C. C., Lee, I., Huang, K., & Chen, W. P. (2010). Efficient astronomical data classification on large-scale distributed systems. In Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings (pp. 430-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6104 LNCS). https://doi.org/10.1007/978-3-642-13067-0_45

Tang, Cheng Hsien ; Wang, Min Feng ; Wang, Wei Jen et al. / Efficient astronomical data classification on large-scale distributed systems. Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings. 2010. pp. 430-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Tang, CH, Wang, MF, Wang, WJ , Tsai, MF, Urata, Y, Ngeow, CC, Lee, I, Huang, K & Chen, WP 2010, Efficient astronomical data classification on large-scale distributed systems. in Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6104 LNCS, pp. 430-440, 5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010, Hualien, Taiwan, 10/05/10. https://doi.org/10.1007/978-3-642-13067-0_45

Efficient astronomical data classification on large-scale distributed systems. / Tang, Cheng Hsien; Wang, Min Feng; Wang, Wei Jen et al.
Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings. 2010. p. 430-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6104 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ngeow, Chow Choong

AU - Lee, Induk

AU - Huang, Kuiyun

AU - Chen, Wen Ping

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AB - 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.

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ER -

Tang CH, Wang MF, Wang WJ , Tsai MF, Urata Y, Ngeow CC et al. Efficient astronomical data classification on large-scale distributed systems. In Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings. 2010. p. 430-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-13067-0_45

Efficient astronomical data classification on large-scale distributed systems

Abstract

Publication series

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Keywords

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