This book constitutes revised selected papers from 10 workshops that were held as the ISC High Performance 2017 conference in Frankfurt, Germany, in June 2017. The 59 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They stem from the following workshops: Workshop on Virtualization in High-Performance Cloud Computing (VHPC) Visualization at Scale: Deployment Case Studies and Experience Reports International Workshop on Performance Portable Programming Models for Accelerators (P^3MA) OpenPOWER for HPC (IWOPH) International Workshop on Data Reduction for Big Scientific Data (DRBSD) International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale Workshop on HPC Computing in a Post Moore's Law World (HCPM) HPC I/O in the Data Center ( HPC-IODC) Workshop on Performance and Scalability of Storage Systems (WOPSSS) IXPUG: Experiences on Intel Knights Landing at the One Year Mark International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale (ExaComm)

How the United States’ regulation of broadband pipelines, digital platforms, and data—together understood as “the cloud”—has eroded civil liberties, democratic principles, and the foundation of the public interest over the past century. Cloud Policy is a policy history that chronicles how the past century of regulating media infrastructure in the United States has eroded global civil liberties as well as democratic principles and the foundation of the public interest. Jennifer Holt explores the long arc of regulating broadband pipelines, digital platforms, and the data centers that serve as the cloud’s storage facilities—an evolution that is connected to the development of nineteenth- and twentieth-century media and networks, including railroads, highways, telephony, radio, and television. In the process, Cloud Policy unearths the lasting inscriptions of policy written for an analog era and markets that no longer exist on the contemporary governance of digital cloud infrastructure. Cloud Policy brings together numerous perspectives that have thus far remained largely siloed in their respective fields of law, policy, economics, and media studies. The resulting interdisciplinary argument reveals a properly scaled view of the massive challenge facing policymakers today. Holt also addresses the evolving role of the state in the regulation of global cloud infrastructure and the growing influence of corporate gatekeepers and private sector self-governance. Cloud policy’s trajectory, as Holt explains, has enacted a transformation in the cultural valuation of infrastructure as civic good, turning it into a tool of commercial profit generation. Despite these current predicaments, the book’s historical lens ultimately helps the reader to envision restorative interventions and new forms of activism to create a more equitable future for infrastructure policy.

This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.

The Internet of Things (IoT) is one of the most disruptive technologies, enabling ubiquitous and pervasive computing scenarios. IoT is based on intelligent self-configuring nodes (also known as things) interconnected in a dynamic and global collaborative network infrastructure. In contrast, Cloud computing has virtually unlimited capabilities in terms of storage and processing power, speed, and is a more mature technology. Due to intrinsic nature of Cloud computing and IoT, they both complement each other. Recently, we are witnessing an increasing trend in exploiting use of both Cloud and IoT together. Salient Features: • Presents latest developments in Cloud computing • Presents latest developments in Internet of Things • Establishes links between interdisciplinary areas where IoT and Cloud both can play a role for improvement of process • Intends to provide an insight into non-IT related models for improvement of lives • Bridges the gap between obsolete literature and current literature This book is aimed primarily at advanced undergraduates and graduates working with IoT and cloud computing. Researchers, academicians, policy makers, government officials, NGOs, and industry research professionals would also find the book useful.

This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.

This book describes OCLC's contributions to the transformation of the Internet from a web of documents to a Web of Data. The new Web is a growing `cloud' of interconnected resources that identify the things people want to know about when they approach the Internet with an information need. The linked data architecture has achieved critical mass just as it has become clear that library standards for resource description are nearing obsolescence. Working for the world's largest library cooperative, OCLC researchers have been active participants in the development of next-generation standards for library resource description. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship. This book focuses on the conceptual and technical challenges involved in publishing linked data derived from traditional library metadata. This transformation is a high priority because most searches for information start not in the library, nor even in a Web-accessible library catalog, but elsewhere on the Internet. Modeling data in a form that the broader Web understands will project the value of libraries into the Digital Information Age. The exposition is aimed at librarians, archivists, computer scientists, and other professionals interested in modeling bibliographic descriptions as linked data. It aims to achieve a balanced treatment of theory, technical detail, and practical application.

Shedding new light on the human side of big data through the lenses of emotional and social intelligence competencies, this book advances the understanding of the requirements of the different professions that deal with big data. It also illustrates the empirical evidence collected through the application of the competency-based methodology to a sample of data scientists and data analysts, the two most in-demand big data jobs in the labor market. The book provides recommendations for the higher education system to offer better designed curricula for entry-level big data professions. It also offers managerial insights in describing how organizations and specifically HR practitioners can benefit from the competency-based approach to overcome the skill shortage that characterizes the demand for big data professional roles and to increase the effectiveness of the selection and recruiting processes.

This book provides a dual perspective on the Internet of Things and ubiquitous computing, along with their applications in healthcare and smart cities. It also covers other interdisciplinary aspects of the Internet of Things like big data, embedded Systems and wireless Sensor Networks. Detailed coverage of the underlying architecture, framework, and state-of the art methodologies form the core of the book.

This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking. To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions. Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.