Data Science
Description
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.
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Content
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.
