Big Scientific Data Management

Jianhui Li

ISBN 10: 3030280608 / ISBN 13: 9783030280604

Published by Springer International Publishing, Springer Nature Switzerland Aug 2019, 2019

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 348 pp. Englisch.

Seller Inventory # 9783030280604

Bibliographic Details

• Title: Big Scientific Data Management
• Publisher: Springer International Publishing, Springer Nature Switzerland Aug 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 3030280608
• ISBN 13: 9783030280604
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 19D millimeters 235 millimeters width by 155 millimeters height by 19 millimeters depth
• Weight: 528 grams

About this title

Synopsis

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.