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Data Science

Editat de Qinglei Zhou, Yong Gan, Weipeng Jing, Xianhua Song, Yan Wang, Zeguang Lu
en Limba Engleză Paperback – 9 sep 2018
This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018.  The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.
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Specificații

ISBN-13: 9789811322020
ISBN-10: 9811322023
Pagini: 788
Ilustrații: XXIX, 755 p. 325 illus.
Dimensiuni: 155 x 235 x 42 mm
Greutate: 1.17 kg
Ediția:1st ed. 2018
Editura: Springer
Locul publicării:Singapore, Singapore

Cuprins

Computational theory for data science.- Big data management and applications.- Data quality and data preparation.- Evaluation and measurement in data science.- Data visualization.- Big data mining and knowledge management.- Infrastructure for data science.- Machine learning for data science.- Data security and privacy.- Applications of data science.- Case study of data science.- Multimedia data management and analysis.- Data-driven scientific research.- Data-driven bioinformatics.- Data-driven healthcare.- Data-driven management.- Data-driven e-government.- Data-driven smart city/planet.- Data marketing and economics.- Social media and recommendation systems.- Data-driven security.- Data-driven business model innovation.- Social and/or organizational impacts of data science.