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Big Data Analytics in Supply Chain Management: Theory and Applications
by Iman Rahimi, Amir H. Gandomi, Simon James Fong, and M. Ali ÜlküIn a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations.From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.
Big Data Analytics Strategies for the Smart Grid
by Carol L. StimmelA comprehensive data analytics program is the only way utilities will be able to meet the challenges of modern grids with operational efficiency, while reconciling the demands of greenhouse gas legislation, and establishing a meaningful return on investment from smart grid deployments. This book addresses the requirements for applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid.
Big Data Analytics Using Multiple Criteria Decision-Making Models (Operations Research Series)
by Ramakrishnan Ramanathan, Muthu Mathirajan, and A. Ravi RavindranMultiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.
Big Data and Artificial Intelligence: 12th International Conference, BDA 2024, Hyderabad, India, December 17–20, 2024, Proceedings (Lecture Notes in Computer Science #15526)
by Anirban Dasgupta Rage Uday Kiran Radwa El Shawi Satish Srirama Mainak AdhikariThis book constitutes the proceedings of the 12th International Conference on Big Data and Artificial Intelligence, BDA 2024, held in Hyderabad, India, during December 17–20, 2024. The 16 full papers and 12 short papers presented here were carefully reviewed and selected from 106 submissions. These papers have been categorized under the following topical sections: Image Classification; Graph Analytics; Big Data Analytics; Applications; Data Science; Health-Care Analytics; eLearning; Prediction and Forecasting.
Big Data and Artificial Intelligence: 11th International Conference, BDA 2023, Delhi, India, December 7–9, 2023, Proceedings (Lecture Notes in Computer Science #14418)
by Vikram Goyal Naveen Kumar Sourav S. Bhowmick Pawan Goyal Navneet Goyal Dhruv KumarThis book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri.
Big Data and Artificial Intelligence for Decision-Making in the Smart Economy (Studies in Big Data #168)
by Aziza B. Karbekova Ladislav Žák Ivan MilenkovićThis book focuses on the systemic scientific-methodological and practical exploration of organizational-technical and socio-economic issues related to the automation of decision-making in the smart economy under Industry 4.0 using big data and artificial intelligence (AI). The scientific novelty of the results presented in the book lies in uncovering the “black box” of decision-making automation in the smart economy through these technologies. The book clarifies the role and significance of big data and AI in decision-making within the smart economy, highlighting its fundamental importance. Additionally, the book thoroughly discusses international experiences in decision-making automation in the smart economy, with examples from Armenia, Kyrgyzstan, and other Central Asian countries, demonstrating its empirical value. The book reveals advanced organizational-economic models for decision-making based on big data and AI. It also presents the latest trends in the development of the smart economy using big data and AI. Moreover, the book explains the socio-ecological and legal aspects of the ethics in applying big data and AI technologies. Additionally, the book proposes prospective applied solutions for decision-making in the smart economy based on big data and AI. The target audience of the book includes scientists studying big data and AI.
Big Data and Artificial Intelligence in Digital Finance: Increasing Personalization and Trust in Digital Finance using Big Data and AI
by John Soldatos Dimosthenis KyriazisThis open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance.
Big Data and Blockchain for Service Operations Management (Studies in Big Data #98)
by Ali Emrouznejad Vincent CharlesThis book aims to provide the necessary background to work with big data blockchain by introducing some novel applications in service operations for both academics and interested practitioners, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book intends to cover theory, research, development, and applications of big data and blockchain, as embedded in the fields of mathematics, engineering, computer science, physics, economics, business, management, and life sciences, to help service operations management.
Big Data and Blockchain Technology for Secure IoT Applications (Advances in Digital Technologies for Smart Applications)
by Shitharth Selvarajan Gouse Baig Mohammad Sadda Bharath Reddy Balachandran Praveen KumarBig Data and Blockchain Technology for Secure IoT Applications presents a comprehensive exploration of the intersection between two transformative technologies: big data and blockchain, and their integration into securing Internet of Things (IoT) applications. As the IoT landscape continues to expand rapidly, the need for robust security measures becomes paramount to safeguard sensitive data and ensure the integrity of connected devices. This book delves into the synergistic potential of leveraging big data analytics and blockchain’s decentralized ledger system to fortify IoT ecosystems against various cyber threats, ranging from data breaches to unauthorized access. Within this groundbreaking text, readers will uncover the foundational principles underpinning big data analytics and blockchain technology, along with their respective roles in enhancing IoT security. Through insightful case studies and practical examples, this book illustrates how organizations across diverse industries can harness the power of these technologies to mitigate risks and bolster trust in IoT deployments. From real‑time monitoring and anomaly detection to immutable data storage and tamper‑proof transactions, the integration of big data and blockchain offers a robust framework for establishing secure, transparent, and scalable IoT infrastructures. Furthermore, this book serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the complexities of IoT security. By bridging the gap between theory and application, this book equips readers with the knowledge and tools necessary to navigate the evolving landscape of interconnected devices while safeguarding against emerging cyber threats. With contributions from leading experts in the field, it offers a forward‑thinking perspective on harnessing the transformative potential of big data and blockchain to realize the full promise of the IoT securely.
Big Data and Cloud Computing: Select Proceedings of ICBCC 2022 (Lecture Notes in Electrical Engineering #1021)
by Neelanarayanan Venkataraman Lipo Wang Xavier Fernando Ahmed F. ZobaaThe book presents papers from the 7th International Conference on Big Data and Cloud Computing Challenges (ICBCC 2022). The book includes high-quality, original research on various aspects of big data and cloud computing, offering perspectives from the industrial and research communities on addressing the current challenges in the field. This book discusses key issues and highlights recent advances in a single broad topic applicable to different sub-fields by exploring various multidisciplinary technologies. This book supports the transfer of vital knowledge to next-generation researchers, students, and practitioners in academia and industry.
Big Data and Data Science Engineering: Volume 6 (Studies in Computational Intelligence #1139)
by Roger LeeThe book reports state-of-the-art results in Big Data and Data Science Engineering in both printed and electronic form. Studies in Computation Intelligence (SCI) has grown into the most comprehensive computational intelligence research forum available in the world. This book publishes original papers on both theory and practice that address foundations, state-of-the-art problems and solutions, and crucial challenges.
Big Data and Edge Intelligence for Enhanced Cyber Defense: Principles and Research (ISSN)
by Akash Kumar Bhoi Ranjit Panigrahi Albuquerque, Victor Hugo C. de K. S. Hareesha Parvathaneni Naga SrinivasuAn unfortunate outcome of the growth of the Internet and mobile technologies has been the challenge of countering cybercrime. This book introduces and explains the latest trends and techniques of edge artificial intelligence (EdgeAI) intended to help cyber security experts design robust cyber defense systems (CDS), including host-based and network-based intrusion detection system and digital forensic intelligence. This book discusses the direct confluence of EdgeAI with big data, as well as demonstrating detailed reviews of recent cyber threats and their countermeasure. It provides computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cyber security big data, in addition to other basic information related to network security. In addition, it provides a brief overview of modern cyber security threats and outlines the advantages of using EdgeAI to counter these threats, as well as exploring various cyber defense mechanisms (CDM) based on detection type and approaches. Specific challenging areas pertaining to cyber defense through EdgeAI, such as improving digital forensic intelligence, proactive and adaptive defense of network infrastructure, and bio-inspired CDM, are also discussed. This book is intended as a reference for academics and students in the field of network and cybersecurity, particularly on the topics of intrusion detection systems, smart grid, EdgeAI, and bio-inspired cyber defense principles. The front-line EdgeAI techniques discussed will also be of use to cybersecurity engineers in their work enhancing cyber defense systems.
Big Data and Electric Mobility
by Quan Zhou Haoran ZhangThis book details how to assess electric mobility characteristics within electric vehicles, discussing energy management methods, automated systems, and the enormous potential of data resources mined from software, navigation systems, and connectivity.Big Data and Electric Mobility presents methods to mine data specifically for electric vehicles, to comprehend their performance and to present opportunities to develop data-driven technological advancements. Including contributions from experts across the world, the book will look at topics such as human mobile behavior, battery charging and health, powertrain simulation, energy management, and multiphysics-constrained optimal charging.The book will be key reading for researchers and engineers in the fields of automotive engineering, electrical engineering, and data mining.
Big Data and Internet of Things: Proceeding of The Seventh International Conference on Big Data and Internet of Things BDIoT'24, Volume 2 (Lecture Notes in Networks and Systems #887)
by Oussama Mahboub Khalid Haddouch Hicham Omara Mostafa Hefnawi"Big Data and Internet of Things" is the latest volume in the renowned Lecture Notes in Networks and Systems series. This book compiles the latest research presented at the Seventh International Conference on Big Data and Internet of Things (BDIoT'24), showcasing innovative solutions, emerging trends, and practical applications in the fields of big data and IoT. An essential read for researchers, professionals, and students looking to stay ahead in the rapidly evolving world of technology. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems. Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems Automation, Manufacturing, Smart Grids. Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.
Big Data and Social Media Analytics
by Mehmet Çakırtaş Mehmet Kemal OzdemirThis edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond. It covers efficient compression of large networks, link prediction in hashtag graphs, visual exploration of social media data, identifying motifs in multivariate data, social media surveillance to enhance search and rescue missions, recommenders for collaborative filtering and safe travel plans to high risk destinations, analysis of cyber influence campaigns on YouTube, impact of location on business rating, bibliographical and co-authorship network analysis, and blog data analytics. All these trending topics form a major part of the state of the art in social media and big data analytics. Thus, this edited book may be considered as a valuable source for readers interested in grasping some of the most recent advancements in this high trending domain.
Big Data Applications and Services 2017: The 4th International Conference on Big Data Applications and Services (Advances in Intelligent Systems and Computing #770)
by Wookey Lee Carson K. LeungThis proceedings volume contains selected papers from the Fourth International Conference on Big Data Applications and Services (BigDAS 2017), held in Tashkent, Uzbekistan on August 15-18, 2017. Big data has become a core technology providing innovative solutions in many fields including social media, healthcare and manufacturing. The Fourth International Conference on Big Data Applications and Services (BigDAS 2017) presented innovative results, encouraged academic and industrial interaction, and promoted collaborative research in the field of big data worldwide. The conference was organized by the Korea Big Data Services Society and National University of Uzbekistan.
Big Data Approach to Firm Level Innovation in Manufacturing: Industrial Economics (SpringerBriefs in Applied Sciences and Technology)
by Seyed Mehrshad Parvin Hosseini Aydin AziziThis book discusses utilizing Big Data and Machine Learning approaches in investigating five aspects of firm level innovation in manufacturing; (1) factors that determine the decision to innovate (2) the extent of innovation (3) characteristics of an innovating firm (4) types of innovation undertaken and (5) the factors that drive and enable different types of innovation. A conceptual model and a cost-benefit framework were developed to explain a firm’s decision to innovate. To empirically demonstrate these aspects, Big data and machine learning approaches were introduced in the form of a case study. The result of Big data analysis as an inferior method to analyse innovation data was also compared with the results of conventional statistical methods. The implications of the findings of the study for increasing the pace of innovation are also discussed.
Big Data, Big Challenges: Background, Issues, Solutions and Research Directions (Lecture Notes in Bioengineering)
by Mowafa Househ Andre W. Kushniruk Elizabeth M. BoryckiThis is the first book to offer a comprehensive yet concise overview of the challenges and opportunities presented by the use of big data in healthcare. The respective chapters address a range of aspects: from health management to patient safety; from the human factor perspective to ethical and economic considerations, and many more. By providing a historical background on the use of big data, and critically analyzing current approaches together with issues and challenges related to their applications, the book not only sheds light on the problems entailed by big data, but also paves the way for possible solutions and future research directions. Accordingly, it offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes; and for students and researchers whose work involves data science-related research issues in healthcare.
Big Data, Cloud Computing, and Data Science Engineering (Studies in Computational Intelligence #1075)
by Roger LeeThis book presents scientific results of the 7th IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2021) which was held on August 4-6, 2022 in Danang, Vietnam. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. All aspects (theory, applications, and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here in the results of the articles featured in this book. The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 15 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.
Big Data, Datafizierung und digitale Artefakte (Medienbildung und Gesellschaft #42)
by Johannes Fromme Dan Verständig Stefan Iske Katrin WildeDer Band fokussiert Entwicklungen und Problemstellungen rund um das Verhältnis des Menschen zu Daten und Zahlen sowie die daran geknüpften Implikationen für Medien, Bildung und Gesellschaft. Ausgangspunkte bilden hierbei auf der einen Seite Big Data und Tendenzen der Datafizierung sozialer Prozesse, auf der anderen Seite Transformationen des Ästhetischen im Hinblick auf kreativ-ästhetische Praktiken. Der Band versammelt dabei unterschiedliche theoretische Positionen, die sich gemeinsam an zentralen Fragen der Medienbildung und kulturellen Bildung im digitalen Zeitalter orientieren.
The Big Data-Driven Digital Economy: Artificial and Computational Intelligence (Studies in Computational Intelligence #974)
by Abdalmuttaleb M. A. Musleh Al-SartawiThis book shows digital economy has become one of the most sought out solutions to sustainable development and economic growth of nations. This book discusses the implications of both artificial intelligence and computational intelligence in the digital economy providing a holistic view on AI education, economics, finance, sustainability, ethics, governance, cybersecurity, blockchain, and knowledge management. Unlike other books, this book brings together two important areas, intelligence systems and big data in the digital economy, with special attention given to the opportunities, challenges, for education, business growth, and economic progression of nations. The chapters hereby focus on how societies can take advantage and manage data, as well as the limitations they face due to the complexity of resources in the form of digital data and the intelligence which will support economists, financial managers, engineers, ICT specialists, digital managers, data managers, policymakers, regulators, researchers, academics, students, economic development strategies, and the efforts made by the UN towards achieving their sustainability goals.
Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
by Yaguo Lei Naipeng Li Xiang LiThis book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at presentPresents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosisProvides abundant experimental validations and engineering cases of the presented methodologies
Big Data for Entrepreneurship and Sustainable Development (Big Data for Industry 4.0)
by Imen Ben Slimene Uğur Özgöker Mohammed El Amine Abdelli Wissem Ajili-Ben YoussefThis book provides insight for researchers and decision-makers on the application of data in the entrepreneurship and sustainable development sector. This book covers how Big Data for Industry 4.0 and entrepreneurship are effective in resolving business, social, and economic problems. The book discusses how entrepreneurs use Big Data to cut costs and minimize the waste of time. It offers how using Big Data can increase efficiency, enables the studying of competitors, can improve the pricing of products, increase sales and loyalty, and can ensure the right people are hired. The book presents how decision-makers can make use of Big Data to resolve economic and social problems. Analyze the development of the economy and enhance the business climate. This book is for researchers, PhD students, and entrepreneurs and can also be of interest for transforming governments as well as businesses.
Big Data for Urban Sustainability
by Stephen Jia Wang Patrick MoriartyThis book presents a practical framework for the application of big data, cloud, and pervasive and complex systems to sustainable solutions for urban environmental challenges. It covers the technologies, potential, and possible and impact of big data on energy efficiency and the urban environment.The book first introduces key aspects of big data, cloud services, pervasive computing, and mobile technologies from a pragmatic design perspective, including sample open source firmware. Cloud services, mobile and embedded platforms, interfaces, operating system design methods, networking, and middleware are all considered. The authors then explore in detail the framework, design principles, architecture and key components of developing energy systems to support sustainable urban environments. The included case study provides a pathway to improve the eco-efficiency of urban transport, demonstrating how to design an energy efficient next generation urban navigation system by leveraging vast cloud data sets on user-behavior. Ultimately, this resource maps big data’s pivotal intersection with rapid global urbanization along the path to a sustainable future.
Big Data in Bioeconomy: Results from the European DataBio Project
by Christian Zinke-Wehlmann Caj Södergård Tomas Mildorf Ephrem Habyarimana Arne J. Berre Jose A. FernandesThis edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen.With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future.With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources.