Mohamed Gaber – författare

As sensors become ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. This book presents innovative solutions in offline data mining and real-time analysis of sensor or geographically distributed data. It discusses the challenges and requirements for sensor data based knowledge discovery solutions in high-priority application illustrated with case studies. It explores the fusion between heterogeneous data streams from multiple sensor types and applications in science, engineering, and security.

This book includes a wide range of topics that deals with international accounting standards, regulations, and financial reporting. The book is a timely collection of several original research papers written by well-known authors and experts in the field from countries around the globe on very important and emerging issues in international accounting. Due to the adoption of “International Accounting Standards (IAS) Regulation” by the European Parliament in 2002 (N0 1606/2002), listed companies in the EU capital markets are required to implement IAS by 2005. This regulation is a revolutionary one, and therefore, there is a need for the type research that focus on the lobbying activities towards the International Accounting Standard Board (IASB). This book includes an in-depth coverage of such lobbying activities as well as an extensive research papers that focus on the content analysis of the comment letters received by International Accounting Standards Board (IASB). Fair value accounting has gained some special interest in recent years. The book includes several research papers on Fair Value Accounting and its application in different countries. Other topics covered in the book include auditing, taxation, and accounting education in several countries. As an added value, the book include several chapters that should be viewed as educational tools for countries that do not have financial and technical resources to establish their own accounting and reporting standards. So, it is of critical importance for such countries to have a model to use for their financial reporting standards, and as such, the book includes research papers that explain how to adopt international financial reporting standards in such countries. Experts in the field from different countries including Australia Belgium, Brazil, Estonia, France, Germany, Italy, Japan, Malaysia, Singapore, New Zealand, United Arab Emirates, United Kingdom, and the United States join forces to bring the latest development in the international accounting standards and financial reporting. This book will be a very valuable resource for colleges and universities that have a course in international accounting as a part of their undergraduate or graduate curriculum. As well, the book will serve as a useful reference at any academic or professional library worldwide.

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security.Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing.After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security.Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing.After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.