A Machine Learning Perspective on Big Data Analysis -- An Insight on Big Data Analytics -- Toward Problem Solving Support based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgment -- An Overview of Concept Drift Applications -- Analysis of Text-Enriched Heterogeneous Information Networks -- Implementing Big Data Analytics Projects in Business -- Data mining in Business: Current Advances and Future Challenges -- Industrial-Scale Ad Hoc Risk Analytics Using MapReduce -- Big Data and the Internet of Things -- Social Network Analysis in Streaming Call Graphs -- Scalable Cloud-Based Data Analysis Software Systems for Big Data -- From Next Generation Sequencing -- Discovering Networks of Interdependent Features in High-Dimensional Problems -- Final Remarks on Big Data Analysis and its Impact on Society and Science

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges