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1. |
Record Nr. |
UNIBAS000012317 |
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Autore |
Cicero, Marcus Tullius <106-43 a.C.> |
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Titolo |
De finibus bonorum et malorum / Cicero ; with an English translation by H. Rackham |
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Pubbl/distr/stampa |
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Cambridge, Massachusetts : Harvard University Press |
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London : <<William>> Heinemann, 1971 |
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Titolo uniforme |
De finibus bonorum et malorum / Cicero, Marcus Tullius |
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ISBN |
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0-674-99044-7 |
0-434-99040-X |
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Descrizione fisica |
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Collana |
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The Loeb classical library |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Traduzione inglese con testo latino a fronte |
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2. |
Record Nr. |
UNINA9910349308103321 |
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Titolo |
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 |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIII, 332 p. 172 illus., 113 illus. in color.) |
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Collana |
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Information Systems and Applications, incl. Internet/Web, and HCI, , 2946-1642 ; ; 11473 |
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Disciplina |
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Soggetti |
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Big data |
Computer networks |
Computer engineering |
Artificial intelligence |
Electronic data processing - Management |
Data protection |
Big Data |
Computer Communication Networks |
Computer Engineering and Networks |
Artificial Intelligence |
IT Operations |
Data and Information Security |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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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Sommario/riassunto |
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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. |
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