Zou / Han / Sun Data Science

Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017, Proceedings, Part II
1. Auflage 2017
ISBN: 978-981-10-6388-6
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 728, 587 Seiten, eBook

Reihe: Communications in Computer and Information Science

This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017.  The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 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, D ata-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.

Zou, Beiji

Han, Qilong

Sun, Guanglu

Jing, Weipeng

Peng, Xiaoning

Lu, Zeguang

Inhaltsverzeichnis

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.