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Big data refers to large and complex massive amounts of data sets that it becomes difficult to process and analyze using traditional data processing technology. Over the past few years there has been an exponential growth in the rate of available data sets obtained from complex systems, ranging from the interconnection of millions of users in social media data, cheminformatics, hydroinformatics to the information contained in the complex biological data sets. This taking and opened new challenges and opportunities to researcher and scientists on how to acquisition, Recording, store and manipulate this huge amount of data sets and how to develop new tools, mining, study, and visualize the massive amount data sets and what insight can we learn from systems that were previously not understood due to the lack of information. All these aspect, coming from multiple disciples under the theme of big data and their features. The ultimate objectives of this volume are to provide challenges and Opportunities to the research communities with an updated, in-depth material on the application of Big data in complex systems in order to finding solutions to the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge. A major investment in Big Data, properly directed, can result not only in major scientific advances, but also lay the foundation for the next generation of advances in science, medicine, and business. The material of this book can be useful to advanced undergraduate and graduate students. Also, researchers and practitioners in the field of big data may benefit from it. Each chapter in the book openes with a chapter abstract and key terms list. The material is organized into seventeen chapters. These chapters are organized along the lines of problem description, related works, and analysis of the results. Comparisons are provided whenever feasible. Each chapter ends with a conclusion and a list of references which is by no means exhaustive. As the editors, we hope that the chapters in this book will stimulate further research in the field of big data. We hope that this book, covering so many different aspects, will be of value for all readers. The contents of this book are derived from the works of many great scientists, scholars, and researchers, all of whom are deeply appreciated. We would like to thank the reviewers for their valuable comments and suggestions, which contribute to enriching this book. Special thanks go to our publisher, Springer, especially for the tireless work of the series editor of Big data sets sereies, Dr. Thomas Ditzinger.