Description
Data Science, 1st ed. 2017
Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22-24, 2017, Proceedings, Part I
Communications in Computer and Information Science Series, Vol. 727
Coordinators: Zou Beiji, Li Min, Wang Hongzhi, Song Xianhua, Xie Wei, Lu Zeguang
Language: EnglishSubject for Data Science:
Keywords
Data analysis; Recommender system; Deep learning; Social media; Social networks; Emotion analysis; Pattern matching; Outlier analysis; Query processing; Data security; Privacy; Crowdsourcing; Convolutional neural network; Spatial-Temporal data; Event detection; Medical Data Management; Wireless sensor networks
Support: Print on demand
Description
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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, 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.
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.