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Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. Applying Integration Techniques and Methods in Distributed Systems is a critical scholarly publication that defines the current state of distributed systems, determines further goals, and presents architectures and service frameworks to achieve highly integrated distributed systems and presents solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting topics such as multimedia, programming languages, and smart environments, this book is ideal for system administrators, integrators, designers, developers, researchers, and academicians.
Systems integrationâ€"the enterprise-wide integration of computer applicationsâ€"offers an enormous opportunity for U.S. firms to capitalize on their strengths in such areas as complex software, networking, and management. In this book, industry leaders, university researchers, and government policymakers discuss what systems integration is, its importance and prospects for growth, why it is expected to define the characteristics of computerization for decades to come, and why the United States is perceived to have a strong competitive advantage.
This book places particular emphasis on issues of model quality and ideas of model testing and validation. Mathematical and computer-based models provide a foundation for explaining complex behaviour, decision-making, engineering design and for real-time simulators for research and training. Many engineering design techniques depend on suitable models, assessment of the adequacy of a given model for an intended application is therefore critically important. Generic model structures and dependable libraries of sub-models that can be applied repeatedly are increasingly important. Applications are drawn from the fields of mechanical, aeronautical and control engineering, and involve non-linear lumped-parameter models described by ordinary differential equations. - Focuses on issues of model quality and the suitability of a given model for a specific application - Multidisciplinary problems within engineering feature strongly in the applications - The development and testing of nonlinear dynamic models is given very strong emphasis
In April 1991 BusinessWeek ran a cover story entitled, "I Can't Work This ?#!!@ Thing," about the difficulties many people have with consumer products, such as cell phones and VCRs. More than 15 years later, the situation is much the same-but at a very different level of scale. The disconnect between people and technology has had society-wide consequences in the large-scale system accidents from major human error, such as those at Three Mile Island and in Chernobyl. To prevent both the individually annoying and nationally significant consequences, human capabilities and needs must be considered early and throughout system design and development. One challenge for such consideration has been providing the background and data needed for the seamless integration of humans into the design process from various perspectives: human factors engineering, manpower, personnel, training, safety and health, and, in the military, habitability and survivability. This collection of development activities has come to be called human-system integration (HSI). Human-System Integration in the System Development Process reviews in detail more than 20 categories of HSI methods to provide invaluable guidance and information for system designers and developers.
Manufacturing, reduced to its simplest form, involves the sequencing of product forms through a number of different processes. Each individual step, known as an unit manufacturing process, can be viewed as the fundamental building block of a nation's manufacturing capability. A committee of the National Research Council has prepared a report to help define national priorities for research in unit processes. It contains an organizing framework for unit process families, criteria for determining the criticality of a process or manufacturing technology, examples of research opportunities, and a prioritized list of enabling technologies that can lead to the manufacture of products of superior quality at competitive costs. The study was performed under the sponsorship of the National Science Foundation and the Defense Department's Manufacturing Technology Program.
Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery. Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.