Big Scientific Data Management: First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers
Author:   Jianhui Li ,  Xiaofeng Meng ,  Ying Zhang ,  Wenjuan Cui
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Volume:   11473
ISBN:  

9783030280604


Pages:   332
Publication Date:   07 August 2019
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Big Scientific Data Management: First International Conference, BigSDM 2018, Beijing, China, November 30 – December 1, 2018, Revised Selected Papers


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Overview

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.

Full Product Details

Author:   Jianhui Li ,  Xiaofeng Meng ,  Ying Zhang ,  Wenjuan Cui
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Volume:   11473
Weight:   0.534kg
ISBN:  

9783030280604


ISBN 10:   3030280608
Pages:   332
Publication Date:   07 August 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of 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.

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