Applications Of Big Data In Large And Small Scale Systems

With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.

PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems

This book constitutes the thoroughly refereed revised selected papers of the First Workshop on Big Data Benchmarks, WBDB 2012, held in San Jose, CA, USA, in May 2012 and the Second Workshop on Big Data Benchmarks, WBDB 2012, held in Pune, India, in December 2012. The 14 revised papers presented were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on benchmarking, foundations and tools; domain specific benchmarking; benchmarking hardware and end-to-end big data benchmarks.

In recent decades, the industrial revolution has increased economic growth despite its immersion in global environmental issues such as climate change. Researchers emphasize the adoption of circular economy practices in global supply chains and businesses for better socio-environmental sustainability without compromising economic growth. Integrating blockchain technology into business practices could promote the circular economy as well as global environmental sustainability. Integrating Blockchain Technology Into the Circular Economy discusses the technological advancements in circular economy practices, which provide better results for both economic growth and environmental sustainability. It provides relevant theoretical frameworks and the latest empirical research findings in the applications of blockchain technology. Covering topics such as big data analytics, financial market infrastructure, and sustainable performance, this book is an essential resource for managers, operations managers, executives, manufacturers, environmentalists, researchers, industry practitioners, students and educators of higher education, and academicians.

The rise of technology in human culture has changed almost every facet of society. Technology is especially useful regarding sustainable development. These technologies can cause significant greenhouse gas reductions and other benefits in terms of logistics and smart cities. New technology applied in this way can greatly help the human effort to restore the environment. Disruptive Technologies and Eco-Innovation for Sustainable Development provides an in-depth look into the new techniques, strategies, and technologies for achieving environmental sustainability through best business and technology practices. The book covers topics such as eco-innovation, green criteria, Agriculture 4.0, and topics related to logic, philosophy, and history of science and technology from the green/sustainable point of view. It is essential for managers, academicians, scientists, students, and researchers in various government, public, and private sectors.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

The convergence of quantum technologies and biomedical intelligence is a frontier of boundless potential. The quantum advancements revolutionize disease detection, personalized medicine, and health monitoring frameworks while confronting the pressing challenge of accountability in machine learning systems within the biomedical domain. How do quantum innovations at the nexus of biomedical intelligence redefine biomedical research and healthcare, addressing critical inquiries such as the transformative potential of quantum computing, machine learning, and sensing technologies? Quantum Innovations at the Nexus of Biomedical Intelligence explores the intricate synergy between quantum mechanics and the biomedical domain. This book elucidates the profound implications and applications arising from the fusion of quantum computing, artificial intelligence, and biomedical sciences. This book introduces biomedical engineering, setting the stage for a deep dive into the transformative role of quantum computing and artificial intelligence. As the narrative unfolds, the text navigates the reader through the uncharted territories of quantum-enhanced machine learning, quantum sensing and their profound impact on diagnostics, personalized medicine, and health monitoring frameworks. The intersection of quantum computing and AI in medical advancements and cybersecurity is illuminated, offering a comprehensive understanding of the multifaceted applications of these cutting-edge technologies. This book is ideal for researchers, scientists, academics, and professionals across diverse disciplines in quantum innovations within biomedical intelligence.

This four-volume set (CCIS 643, 644, 645, 646) constitutes the refereed proceedings of the 16th Asia Simulation Conference and the First Autumn Simulation Multi-Conference, AsiaSim / SCS AutumnSim 2016, held in Beijing, China, in October 2016. The 265 revised full papers presented were carefully reviewed and selected from 651 submissions. The papers in this third volume of the set are organized in topical sections on Cloud technologies in simulation applications; fractional calculus with applications and simulations; modeling and simulation for energy, environment and climate; SBA virtual prototyping engineering technology; simulation and Big Data.