**Meet the editors**

Sebastián Ventura received his BSc and PhD degrees in science from the University of Cordoba, Spain, in 1989 and 1996, respectively. He is currently a full-time professor of Computing Science and Artificial Intelligence at the University of Cordoba, where he heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than 200 papers in

journals and scientific conferences, and he has edited three books and several special issues in international journals. He has also been engaged in 12 research projects (being the coordinator of four of them) supported by the Spanish and Andalusian governments and the European Union. His main research interests are in the fields of machine learning, data mining, data science, big data, soft computing, and their applications.

Dr. Jose M. Luna received his PhD in Computer Science in January 2014. He has published 17 journal articles in highly prestigious international journals and is the author of the book "Pattern Mining with Evolutionary Algorithms". Additionally, he has written two book chapters and published almost 30 articles in international and national conferences. His h-index is 11, and his

papers have been cited more than 400 times according to Google Scholar.

Alberto Cano is an assistant professor in the Department of Computer Science at the Virginia Commonwealth University, USA, where he heads the High-Performance Data Mining Lab. He received his PhD degree in Computer Science from the University of Granada (Spain) in 2014 and MSc degree from the University of Cordoba (Spain) in 2010. His research is focused on machine

learning, data mining, soft computing, parallel computing, and general purpose GPU systems. He has published 17 articles in international journals, 12 contributions to international conferences, and 2 book chapters, and he has served as a member of the technical program committee in more than 60 international conferences.

## Contents

#### **Preface XI**


## Preface

As technology advances, high volumes of valuable data are generated day by day in mod‐ ern organizations. The management of such huge volumes of data has become a priority in these organizations, requiring new techniques for data management and data analysis in Big Data environments. These environments encompass many different fields including medi‐ cine, education data, and recommender systems.

The term Big Data is being increasingly used almost everywhere and by everyone nowa‐ days. This buzzword is related to the emerging techniques used to manage, explore, and make sense huge volumes of data that cannot be handled by traditional techniques.

This book brings together some of the most interesting fields in which Big Data is essential. The aim of the book is to provide the reader with a variety of fields and systems where the analysis and management of massive datasets are crucial. This book describes the impor‐ tance of the Big Data era and how existing information systems are required to be adapted to face up the problems derived from the management of massive datasets.

The book is divided into a series of chapters including a detailed description about the im‐ portance of Big Data in different fields. The book includes an important analysis about the significance of Big Data in hospital information systems. The complex, distributed, and highly interdisciplinary nature of medical data has underscored the limitations of traditional data analysis capabilities of data accessing, storage, processing, analyzing, distributing, and sharing. The analysis is also extended in another chapter to examine patient characteristics, treatment patterns, and survival among the elderly with acute myeloid leukemia.

The book also includes a different and interesting field in which the term Big Data is the cornerstone, the educational field. The book includes different analyses to provide insights to different stakeholders and thereby foster data-driven actions concerning quality improve‐ ment in education.

The reader will verify that, depending on the application domain, the size of data can vary from megabytes to petabytes, and the term Big Data may refer to different sizes and types depending on the context. However, regardless of the application domain, the common challenge is the same, that is, to being able to make sense of the data by processing them in a high analytical level.

> **Sebastian Ventura** Professor in Computer Science and Artificial Intelligence University of Cordoba Cordoba (SPAIN)
