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Record Nr. |
UNINA9910299295603321 |
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Titolo |
Data Analytics and Management in Data Intensive Domains : XIX International Conference, DAMDID/RCDL 2017, Moscow, Russia, October 10–13, 2017, Revised Selected Papers / / edited by Leonid Kalinichenko, Yannis Manolopoulos, Oleg Malkov, Nikolay Skvortsov, Sergey Stupnikov, Vladimir Sukhomlin |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XV, 281 p. 82 illus.) |
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Collana |
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Communications in Computer and Information Science, , 1865-0937 ; ; 822 |
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Disciplina |
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Soggetti |
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Data mining |
Artificial intelligence |
Database management |
Machine theory |
Natural language processing (Computer science) |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
Database Management |
Formal Languages and Automata Theory |
Natural Language Processing (NLP) |
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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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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Data Analytics -- Next Generation Genomic Sequencing: Challenges and Solutions -- Novel Approaches to Analyzing and Classifying of Various Astronomical Entities and Events -- Ontology Population in Data Intensive Domains -- Heterogeneous Data Integration Issues -- Data Curation and Data Provenance Support -- Temporal Summaries Generation. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, held in Moscow, Russia, in |
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October 2017. The 16 revised full papers presented together with three invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: data analytics; next generation genomic sequencing: challenges and solutions; novel approaches to analyzing and classifying of various astronomical entities and events; ontology population in data intensive domains; heterogeneous data integration issues; data curation and data provenance support; and temporal summaries generation. |
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