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Gain Powerful Insights with SAS Viya! Whether you are an executive, departmental decision maker, or analyst, the need to leverage data and analytical techniques in order make critical business decisions is now crucial to every part of an organization. Smart Data Discovery with SAS Viya: Powerful Techniques for Deeper Insights provides you with the necessary knowledge and skills to conduct a smart discovery process and empower you to ask more complex questions using your data. The book highlights key components of a smart data discovery process utilizing advanced machine learning techniques, powerful capabilities from SAS Viya, and finally brings it all together using real examples and applications. With its step-by-step approach and integrated examples, the book provides a relevant and practical guide to insight discovery that goes beyond traditional charts and graphs. By showcasing the powerful visual modeling capabilities of SAS Viya, it also opens up the world of advanced analytics and machine learning techniques to a much broader set of audiences.
The internet of things (IoT) has massive potential to transform current business models and enhance human lifestyles. With the current pace of research, IoT will soon find many new horizons to touch. IoT is now providing a base of technological advancement in various realms such as pervasive healthcare, smart homes, smart cities, connected logistics, automated supply chain, manufacturing units, and many more. IoT is also paving the path for the emergence of the digital revolution in industrial technology, termed Industry 4.0. Transforming the Internet of Things for Next-Generation Smart Systems focuses on the internet of things (IoT) and how it is involved in modern day technologies in a variety of domains. The chapters cover IoT in sectors such as agriculture, education, business and management, and computer science applications. The multi-disciplinary view of IoT provided within this book makes it an ideal reference work for IT specialists, technologists, engineers, developers, practitioners, researchers, academicians, and students interested in how IoT will be implemented in the next generation of smart systems and play an integral role in advancing technology in the future.
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present
Beginning in the mid-2010s, the Fourth Industrial Revolution has seen remarkable changes in information technology that have blurred the boundaries between the physical, digital and biological worlds. Industry 4.0 has enabled so-called smart factories in which computer systems equipped with machine learning algorithms can learn and control robotics with minimal need for human input. While smart technology has enabled many manufacturing businesses to increase efficiency and cut costs, many others are still struggling with implementing it. This book aims to help students, practitioners and industry leaders to become change agents and take their first steps on the path of transformation. Smart Business and Digital Transformation addresses the challenge of becoming "smart" from three different perspectives: smart factory, smart industry and smart environment. Covering technologies including the Internet of Things (IoT), cloud, artificial intelligence (AI), mobility, 5G and big data analytics, the book shows how enterprises can take advantage of them and ultimately beat the competition. The book considers the importance of operational processes, business models and organisational culture. The contributing authors and editors, based at Corvinus University, present a multidimensional picture of Industry 4.0 which is both diverse in its voices and unified in its vision. Smart Business and Digital Transformation meets the growing demand for a textbook that not only presents the latest concepts and theories but is also practical for planning, managing and implementing digital transformation in practice. The chapters include case studies to demonstrate the practical applications, and each chapter ends with review and discussion questions to develop students’ skills and competencies. Students of business and digital transformation on advanced undergraduate and MBA courses will find it an indispensable guide to a vibrant and challenging topic.
Mr.Aashish Gadgil, Assistant Professor, Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India. Dr.Anjana Sangwan, Associate Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India. Prof.Sulakshana Sagar Malwade, Professor, Department of Polytechnic, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India. Prof.Megha Ashok Dhotay, Professor, Department of Polytechnic, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India.
Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.
This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.
This book constitutes the refereed proceedings of the 7th International Conference on Big Data analytics, BDA 2019, held in Ahmedabad, India, in December 2019. The 25 papers presented in this volume were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; search and information extraction; predictive analytics in medical and agricultural domains; graph analytics; pattern mining; and machine learning.
The roles of corporate and public stewards and the nature of their social contract with society have been changing over the past two centuries, and those changes have accelerated in recent decades. Moreover, with increasing focus on sustainability factors from the marketplace (regulators, investors, financiers, and consumers), corporate sustainability disclosure is shifting from voluntary to vital. Corporate and public stewards are now responsible for their performance and services from cradle-to-grave: they must properly manage corporate social responsibility and integrate it into their global strategies, rather than consider it as merely a moral obligation or a risk/reputation management exercise. Sustainability analytics, the critical link between sustainability and business strategy, helps professionals track, trend, and transform sustainability information into actionable insights across the value chain and life cycle, to enhance their sustainability performance and its disclosure. This book, Introduction to Sustainability Analytics, provides corporate and public stewards with a comprehensive understanding of how to determine which sustainability metrics are material to them and relevant to their business, and how to incorporate them into corporate strategy, resource allocation, and prioritization. Focusing on practical decision-making needs, it explains how to value and prioritize initiatives, and how to best allocate necessary resources through several real case studies and practical examples. Features: Examines pressing issues such as climate change, water scarcity, and environmental justice Explains how to develop a business case and global strategy for social responsibility Includes both corporate and public policy perspectives on sustainability economics Covers emerging regulations on sustainability disclosure and responsible investing
This book contains the refereed proceedings of the 12th International Conference on Knowledge Management in Organizations, KMO 2017, held in Beijing, China, in August 2017. The theme of the conference was "Emerging Technology and Knowledge Management in Organizations." The 45 contributions accepted for KMO 2017 were selected from 112 submissions and are organized in topical sections on: Knowledge Management Models and Behaviour Studies; Knowledge Sharing; Knowledge Transfer and Learning; Knowledge and Service Innovation; Knowledge and Organization; Information Systems Research; Value Chain and Supply Chain; Knowledge Re-presentation and Reasoning; Data Mining and Intelligent Science; Big Data Management; Internet of Things and Network.