Data Science: 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Zhengzhou, China, September 21-23, 2018, Proceedings, Part II

by Qinglei Zhou (Editor), Qiguang Miao (Editor), Hongzhi Wang (Editor), Wei Xie (Editor), Yan Wang (Editor)

Overview

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.

Product Details

ISBN-13:	9789811322068
Publisher:	Springer-Verlag New York, LLC
Publication date:	09/10/2018
Series:	Communications in Computer and Information Science , #902
Sold by:	Barnes & Noble
Format:	eBook
File size:	15 MB
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Table of Contents

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.