Big Scientific Data Management First International Conference, BigSDM 2018, Beijing, China, November 30 - December 1, 2018, Revised Selected Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Li, Jianhui (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Meng, Xiaofeng (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Zhang, Ying (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Cui, Wenjuan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Du, Zhihui (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Information Systems and Applications, incl. Internet/Web, and HCI ; 11473
• Θέματα: Big data.; Computers.; Computer organization.; Artificial intelligence.; Management information systems.; Computer science.; Computer security.; Big Data.; Information Systems and Communication Service.; Computer Systems Organization and Communication Networks.; Artificial Intelligence.; Management of Computing and Information Systems.; Systems and Data Security.

 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.