Zhou / Miao / Wang Data Science

4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Zhengzhou, China, September 21-23, 2018, Proceedings, Part II
Erscheinungsjahr 2018
ISBN: 978-981-13-2206-8
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 649 Seiten

Reihe: Communications in Computer and Information Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Zhou, Qinglei

Miao, Qiguang

Wang, Hongzhi

Xie, Wei

Wang, Yan

Lu, Zeguang

Inhaltsverzeichnis

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.