Big Scientific Data Management : 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):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Information Systems and Applications, incl. Internet/Web, and HCI ; 11473 | Springer eBooks Computer SciencePublisher: Cham : Springer, 2019Edition: 1st ed. 2019Description: 1 Online-Ressource (XIII, 332 p. 172 illus., 113 illus. in color)ISBN:

9783030280611

Subject(s):

Additional physical formats: 9783030280604 | Erscheint auch als: 978-3-030-28060-4 Druck-AusgabeDDC classification:

005.7 23

LOC classification:

QA76.9.B45

DOI: DOI: 10.1007/978-3-030-28061-1Online resources:

Summary: 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 policiesSummary: 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 policiesPPN: PPN: 1676308164Package identifier: Produktsigel: ZDB-2-LNC | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-SEB

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