Frontiers in data science / [edited by] Matthias Dehmer and Frank Emmert-Streib.

Includes bibliographical references and index.

1. Legal aspects of information science, data science, and big data --
2. Legal and policy aspects of information science in emerging automated environments --
3. Privacy as secondary rule, or the intrinsic limits of legal orders in the age of big data --
4. Data ownership: taking stock and mapping the issues --
5. Philosophical and methodological foundations of text data analysis --
6. Mobile commerce and the consumer information paradox: a review of practice, theory, and a research agenda --
7. The impact of big data on making evidence-based decisions --
8. Automated business analytics for artificial intelligence in Big Data@X 4.0 era --
9. The evolution of recommender systems: from the beginning to the big data era --
10. Preprocessing in big data: new challenges for discretization and feature selection --
11. Causation, probability, and all that: data science as a novel inductive paradigm --
12. Big data in healthcare in China: applications, obstacles, and suggestions.

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.