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Big Scientific Data Management [electronic resource] : First International Conference, BigSDM 2018, Beijing, China, November 30 - December 1, 2018, Revised Selected Papers / edited by Jianhui Li, Xiaofeng Meng, Ying Zhang, Wenjuan Cui, Zhihui Du.

Contributor(s): Li, Jianhui [editor.] | Meng, Xiaofeng [editor.] | Zhang, Ying [editor.] | Cui, Wenjuan [editor.] | Du, Zhihui [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Information Systems and Applications, incl. Internet/Web, and HCI: 11473Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XIII, 332 p. 172 illus., 113 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030280611.Subject(s): Big data | Computer networks  | Computer engineering | Artificial intelligence | Electronic data processing -- Management | Data protection | Big Data | Computer Communication Networks | Computer Engineering and Networks | Artificial Intelligence | IT Operations | Data and Information SecurityAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.7 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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