國立虎尾科技大學 |

Big Scientific Data Management = First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Big Scientific Data Management/ edited by Jianhui Li, Xiaofeng Meng, Ying Zhang, Wenjuan Cui, Zhihui Du.
其他題名:	First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers /
其他作者:	Li, Jianhui.
面頁冊數:	XIII, 332 p. 172 illus., 113 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-3-030-28061-1
ISBN:	9783030280611

Big Scientific Data Management = First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers /
Big Scientific Data ManagementFirst International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers /[electronic resource] :edited by Jianhui Li, Xiaofeng Meng, Ying Zhang, Wenjuan Cui, Zhihui Du. - 1st ed. 2019. - XIII, 332 p. 172 illus., 113 illus. in color.online resource. - Information Systems and Applications, incl. Internet/Web, and HCI ;11473. - Information Systems and Applications, incl. Internet/Web, and HCI ;9149.

Application cases in the big scientific data management -- Paradigms for enhancing scientific discovery through big data -- Data management challenges posed by big scientific data -- Machine learning methods to facilitate scientific discovery -- Science platforms and storage systems for large scale scientific applications -- Data cleansing and quality assurance of science data -- Data policies.

This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018. The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies.

ISBN: 9783030280611

Standard No.: 10.1007/978-3-030-28061-1doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Big Scientific Data Management = First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers /
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