Frontiers in Data Science

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Matthias Dehmer (UMIT, Hall in Tirol, Austria) Frank Emmert-Streib (Tampere University of Technology, Germany)

Frontiers in Data Science

Matthias Dehmer (UMIT, Hall in Tirol, Austria), Frank Emmert-Streib (Tampere University of Technology, Germany)

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English

CRC Press
30 September 2020

Econometrics; Automatic control engineering; Databases; Mathematical theory of computation; Machine learning

Series: Chapman & Hall/CRC Big Data Series

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.

Edited by: Matthias Dehmer (UMIT Hall in Tirol Austria), Frank Emmert-Streib (Tampere University of Technology, Germany)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9780367657659
ISBN 10: 0367657651
Series: Chapman & Hall/CRC Big Data Series
Pages: 383
Publication Date: 30 September 2020
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Paperback
Publisher's Status: Active

Preface. Generalized optimal wavelet decomposing algorithm for big financial data. How ’big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Data Science and Big Data: current state and future opportunities. Legal Aspects of Information Science, Data Science and Big Data. Between two hypes: FORZA Digital forensics investigation framework that incorporate legal issues. Application of big data in Intelligent Transportation. Legal and policy aspects of space situational awareness. Causation, probability and all that - Data Science as a novel kind of inductive methodology. Preprocessing in big data: new challenges for discretization and feature selection. The impact of Big Data on making evidence-based decisions. Living in a big data world: Predicting mobile commerce activity through privacy concerns. Privacy as virtue: The negative and the positive obligations of the state. Beyond the hype: Big data concepts, methods, and analytics. Recommendation System for Designing Education Courses: A Data Science Perspective. Between two hypes: Will big data help unravel blind spots in understanding the global land rush?

Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also has a post at Bundeswehr Universit¨at M¨unchen (Germany). His research interests are in Data Science, Big Data, Complex Networks, Machine Learning and Information Theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology. He has more than 205 publications in applied mathematics, computer science and related disciplines. Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral fellow in the USA before becoming a Faculty member at the Center for Cancer Research at the Queen’s University Belfast (UK). Currently, he is a Professor at Tampere University Technology, Finland, in the Department of Signal Processing. His research interests are in the field of computational biology, data science and analytics in the development and application of methods from statistics and machine learning for the analysis of big data from genomics, finance and business.