Service orchestration techniques combine the benefits of Service Oriented Architecture (SOA) and Business Process Management (BPM) to compose and coordinate distributed software services. On the other hand, Software-as-a-Service (SaaS) is gaining popularity as a software delivery model through cloud platforms due to the many benefits to software vendors, as well as their customers. Multi-tenancy, which refers to the sharing of a single application instance across multiple customers or user groups (called tenants), is an essential characteristic of the SaaS model. Written in an easy to follow style with discussions supported by real-world examples, Service Orchestration as Organization introduces a novel approach with associated language, framework, and tool support to show how service orchestration techniques can be used to engineer and deploy SaaS applications.

This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.

This book presents a review of traditional context-aware computing research, identifies its limitations in developing social context-aware pervasive systems, and introduces a new technology framework to address these limitations. Thus, this book provides a good reference for developments in context-aware computing and pervasive social computing. It examines the emerging area of pervasive social computing, which is a novel collective paradigm derived from pervasive computing, social media, social networking, social signal processing and multimodal human-computer interaction. This book offers a novel approach to model, represent, reason about and manage different types of social context. It shows how users' social context information can be acquired from different online social networks such as Facebook, LinkedIn, Twitter and Google Calendar. It further presents the use of social context information in developing innovative smart mobile applications to assist users in their daily life. The mix of both theoretical and applied research results makes this book attractive to a variety of readers from both academia and industry. This book provides a new platform for implementing different types of socially-aware mobile applications. The platform hides the complexity of managing social context, and thus provides essential support to application developers for the development of socially-aware applications. The book contains detailed descriptions of how the underlying platform has been implemented using available technologies such as ontology and rule engines, and how this platform can be used to develop socially-aware mobile applications using two exemplar applications. The book also presents evaluations of the proposed platform and applications using real-world data from Facebook, LinkedIn and Twitter. Therefore, this book is a syndication of scientific research with practical industrial applications, making it useful to researchers as well as to software engineers.

Pancreas transplantation has rapidly moved from an experimental procedure associated with high rates of morbidity and mortality to a mainstream technique with excellent patient and graft survival. Over 30,000 pancreas transplants have already been performed. The value of pancreas transplantation however must be balanced against the risk of the operative procedure and the innovative long-term immunosuppressive therapy. The indications for this procedure and the selection of the patients are critical to ensure low mortality and any improvements in quality of life.
This second edition reflects recent advances in the field, especially the increasing number of islet transplantations and the growing interest in stem cell research applicable to this condition. It provides an authoritative account on the current status of the whole organ pancreas transplantation and islet and pancreatic stem cell transplantation.

Stem cell biology has drawn tremendous interest in recent years as it promises cures for a variety of incurable diseases. This book deals with the basic and clinical aspects of stem cell research and involves work on the full spectrum of stem cells isolated today. It also covers the conversion of stem cell types into a variety of useful tissues which may be used in the future for transplantation therapy. It is thus aimed at undergraduates, postgraduates, scientists, embryologists, doctors, tissue engineers and anyone who wishes to gain some insight into stem cell biology. This book is important as it is comprehensive and covers all aspects of stem cell biology, from basic research to clinical applications. It will have 33 chapters written by renowned stem cell scientists worldwide. It will be up-to-date and all the chapters include self-explanatory figures, color photographs, graphics and tables. It will be easy to read and give the reader a complete understanding and state of the art of the exciting science and its applications.

This book presents a review of traditional context-aware computing research, identifies its limitations in developing social context-aware pervasive systems, and introduces a new technology framework to address these limitations. Thus, this book provides a good reference for developments in context-aware computing and pervasive social computing. It examines the emerging area of pervasive social computing, which is a novel collective paradigm derived from pervasive computing, social media, social networking, social signal processing and multimodal human-computer interaction. This book offers a novel approach to model, represent, reason about and manage different types of social context. It shows how users' social context information can be acquired from different online social networks such as Facebook, LinkedIn, Twitter and Google Calendar. It further presents the use of social context information in developing innovative smart mobile applications to assist users in their daily life. The mix of both theoretical and applied research results makes this book attractive to a variety of readers from both academia and industry. This book provides a new platform for implementing different types of socially-aware mobile applications. The platform hides the complexity of managing social context, and thus provides essential support to application developers for the development of socially-aware applications. The book contains detailed descriptions of how the underlying platform has been implemented using available technologies such as ontology and rule engines, and how this platform can be used to develop socially-aware mobile applications using two exemplar applications. The book also presents evaluations of the proposed platform and applications using real-world data from Facebook, LinkedIn and Twitter. Therefore, this book is a syndication of scientific research with practical industrial applications, making it useful to researchers as well as to software engineers.

This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.