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Big Scientific Data Management: First International Conference, BigSDM 2018, Beijing, China, November 30 December 1, 2018, Revised Selected Papers 2019 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 235x155 mm, kaal: 534 g, 113 Illustrations, color; 59 Illustrations, black and white; XIII, 332 p. 172 illus., 113 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 11473
  • Ilmumisaeg: 07-Aug-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030280608
  • ISBN-13: 9783030280604
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Big Scientific Data Management: First International Conference, BigSDM 2018, Beijing, China, November 30 December 1, 2018, Revised Selected Papers 2019 ed.Suurem pilt
  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 235x155 mm, kaal: 534 g, 113 Illustrations, color; 59 Illustrations, black and white; XIII, 332 p. 172 illus., 113 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 11473
  • Ilmumisaeg: 07-Aug-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030280608
  • ISBN-13: 9783030280604
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030280604.html
Märksõnad:
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.

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.