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Applications of Big Data Analytics: Trends, Issues, and Challenges

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery.Topics and features:Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicingExplores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plantsDescribes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenariosProposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disordersReviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree verticesPresents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessmentThis practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects.Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

Applications of Block Chain technology and Artificial Intelligence: Lead-ins in Banking, Finance, and Capital Market (Financial Mathematics and Fintech)

Today, emerging technologies offer a new pathway for advancing the economy in the fields of banking, finance, and capital markets. Blockchain applications play a crucial role in ensuring trust and security within these industries by relying on transparency and visibility through peer-to-peer networks. The banking industry has also witnessed increased operations speed, better transparency, efficiency enhancement, fraud extenuation at less cost while sharing real-time data between various parties. Thus, the adoption of blockchain in the Banking and Insurance industry is developing very fast. It has emerged as the commonly accepted default platform for the banking and insurance industry. This book explores how blockchain technology optimizes and integrates transactions and operations, facilitating easier access to information. This, in turn, has the potential to reduce communication costs and minimize minor data transfer errors. Additionally, the book delves into the current applications of blockchain technology in the financial industry, discusses its limitations, and outlines its future prospects for broader accessibility. This book is aimed at students and researchers in financial engineering and fintech and it can serve as a reference for identifying problem areas and their possible solutions.

Applications of Blockchain Technology: An Industry Focus

Blockchain technology is a disruptive technology that affords businesspeople an opportunity to correct problems of dishonesty, corruption, and poor decision-making. This book crafts blockchain technology for the non-technical expert, to interpret the various applications of blockchain technology through the lens of various industries and creates opportunities for professionals using practical applications and case studies. Blockchain technology is an important platform for businesses to consider as it provides a consensus based, trusted, and transparent system for businesses to operate various critical functions. As such, this book is a first of its kind to take a deep dive into the application of blockchain technology in different sectors. Applications of blockchain technology is explored through understanding of implementation and configuring the use of blockchain technology in real business application. The book provides access to disseminate blockchain technology and its application in a clear and structured manner by assimilating real-world-cases by providing valuable information for business audiences for all business sectors.

Applications of Blockchain Technology: An Industry Focus

Blockchain technology is a disruptive technology that affords businesspeople an opportunity to correct problems of dishonesty, corruption, and poor decision-making. This book crafts blockchain technology for the non-technical expert, to interpret the various applications of blockchain technology through the lens of various industries and creates opportunities for professionals using practical applications and case studies. Blockchain technology is an important platform for businesses to consider as it provides a consensus based, trusted, and transparent system for businesses to operate various critical functions. As such, this book is a first of its kind to take a deep dive into the application of blockchain technology in different sectors. Applications of blockchain technology is explored through understanding of implementation and configuring the use of blockchain technology in real business application. The book provides access to disseminate blockchain technology and its application in a clear and structured manner by assimilating real-world-cases by providing valuable information for business audiences for all business sectors.

Applications of Blockchain Technology in Business: Challenges and Opportunities (SpringerBriefs in Operations Management)

The book discusses the various ways that blockchain technology is changing the future of money, transactions, government, and business. The first two chapters walk through the foundation of blockchain. Chapters 3–12 look at applications of blockchain in different industries and highlight its exciting new business applications. It show why so many companies are implementing blockchain, and present examples of companies who have successfully employed the technology to improve efficiencies and reduce costs. Chapter 13 highlights blockchain’s powerful potential to foster emerging markets and economies including smart cities, value-based healthcare, decentralized sharing economy, machine to machine transactions, data-sharing marketplace, etc. Chapter 14 offers a conceptual model, provides information and insights, and covers a step-by-step approach to plan and develop blockchain-based technology.

Applications of Computational Intelligence in Data-Driven Trading

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Applications of Computational Intelligence in Data-Driven Trading

“Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.” – Prof. Terrence J. Sejnowski, Computational Neurobiologist The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry. The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic: The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence. The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance. The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry. The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author’s two decades of professional experience as a technologist, quant and academic.

Applications of Data-Centric Science to Social Design: Qualitative and Quantitative Understanding of Collective Human Behavior (Agent-Based Social Systems #14)

The intention behind this book is to illustrate the deep relation among human behavior, data-centric science, and social design. In fact, these three issues have been independently developing in different fields, although they are, of course, deeply interrelated to one another. Specifically, fundamental understanding of human behavior should be employed for investigating our human society and designing social systems. Insights and both quantitative and qualitative understandings of collective human behavior are quite useful when social systems are designed. Fundamental principles of human behavior, theoretical models of human behavior, and information cascades are addressed as aspects of human behavior. Data-driven investigation of human nature, social behavior, and societal systems are developed as aspects of data-centric science. As design aspects, how to design social systems from heterogeneous memberships is explained. There is also discussion of these three aspects—human behavior, data-centric science, and social design—independently and with regard to the relationships among them.

Applications of Data Management and Analysis: Case Studies in Social Networks and Beyond (Lecture Notes in Social Networks #27)

This book addresses and examines the impacts of applications and services for data management and analysis, such as infrastructure, platforms, software, and business processes, on both academia and industry. The chapters cover effective approaches in dealing with the inherent complexity and increasing demands of big data management from an applications perspective.Various case studies included have been reported by data analysis experts who work closely with their clients in such fields as education, banking, and telecommunications. Understanding how data management has been adapted to these applications will help students, instructors and professionals in the field. Application areas also include the fields of social network analysis, bioinformatics, and the oil and gas industries.

Applications of Data Mining in Computer Security (Advances in Information Security #6)

Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.

Applications of Data Mining to Electronic Commerce

Applications of Data Mining to Electronic Commerce brings together in one place important contributions and up-to-date research results in this fast moving area. Applications of Data Mining to Electronic Commerce serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Applications of Databases: First International Conference, ADB-94, Vadstena, Sweden, June 21 - 23, 1994. Proceedings (Lecture Notes in Computer Science #819)

This volume presents the proceedings of the First International Conference on Applications of Databases, ADB-94, held at Vadstena, Sweden in June 1994. ADB-94 provided a unique platform for the discussion of innovative applications of databases among database researchers, developers and application designers. The 28 refereed papers were carefully selected from more than 100 submissions. They report on DB applications, for example in air traffic, modelling, maps, environment, finance, engineering, electronic publishing, and digital libraries, and they are devoted to advanced database services, as for example image text and multimedia modelling, fuzzy set based querying, knowledge management, heterogeneous multidatabase management, and intelligent networks.

Applications of Decision Science in Management: Proceedings of International Conference on Decision Science and Management (ICDSM 2022) (Smart Innovation, Systems and Technologies #260)

This book covers research trends of data science and management involving cutting edge technologies and novel research directions from diverse fields of industries, business and government sectors. It involves usage of various advanced tools and techniques for understanding different data collected at the grassroot level to generate actionable insights for making crucial decisions. This book aims to serve as a reference book for researchers in the area of decision science for management. It covers alternative solutions with innovative ideas and issues from different fields of business management.

Applications of Emerging Technologies and AI/ML Algorithms: International Conference on Data Analytics in Public Procurement and Supply Chain (ICDAPS2022) (Asset Analytics)

This book provides practical insights into applications of the state-of-the-art of Machine Learning and Artificial Intelligence (AI) for solving intriguing and complex problems in procurement and supply chain management. The application domain includes perishable food supply chain, steel price prediction, electric vehicle charging infrastructure design, contract price negotiation, reverse logistics network design, and demand forecasting. Further, the book highlights the advanced topics in the procurement field, like AI in green procurement and e-procurement in the pharma sector. Furthermore, the book covers applications of well-established methodologies such as heuristics, optimization, game theory, and MCDM based on the nature of the problem. The inclusion of the vaccine supply chain digital twin and blockchain-based procurement signals the significance of the book. This book is a comprehensive guide for industry professionals to understand the power of data analytics, enabling them to improve efficiency and effectiveness in the procurement and supply chain sectors.

Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM, Como, Italy, April 18-20, 2001 Proceedings (Lecture Notes in Computer Science #2037)

Applications of Fourier Transform to Smile Modeling: Theory and Implementation (Springer Finance)

This book addresses the applications of Fourier transform to smile modeling. Smile effect is used generically by ?nancial engineers and risk managers to refer to the inconsistences of quoted implied volatilities in ?nancial markets, or more mat- matically, to the leptokurtic distributions of ?nancial assets and indices. Therefore, a sound modeling of smile effect is the central challenge in quantitative ?nance. Since more than one decade, Fourier transform has triggered a technical revolution in option pricing theory. Almost all new developed option pricing models, es- cially in connection with stochastic volatility and random jump, have extensively applied Fourier transform and the corresponding inverse transform to express - tion pricing formulas. The large accommodation of the Fourier transform allows for a very convenient modeling with a general class of stochastic processes and d- tributions. This book is then intended to present a comprehensive treatment of the Fourier transform in the option valuation, covering the most stochastic factors such as stochastic volatilities and interest rates, Poisson and Levy ´ jumps, including some asset classes such as equity, FX and interest rates, and providing numerical ex- ples and prototype programming codes. I hope that readers will bene?t from this book not only by gaining an overview of the advanced theory and the vast large l- erature on these topics, but also by gaining a ?rst-hand feedback from the practice on the applications and implementations of the theory.

Applications of Graph Transformations with Industrial Relevance: International Workshop, AGTIVE'99 Kerkrade, The Netherlands, September 1-3, 1999 Proceedings (Lecture Notes in Computer Science #1779)

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Graph Transformation with Industrial Relevance, AGTIVE'99, held in Kerkrade, The Netherlands, in June 1999.The 28 revised full papers presented went through an iterated process of reviewing and revision. Also included are three invited papers, 10 tool demonstrations, a summary of a panel discussion, and lists of graph transformation systems and books on graph transformations. The papers are organized in sections on modularization concepts, distributed systems modeling, software architecture: evolution and reengineering, visual graph transformation languages, visual language modeling and tool development, knowledge modeling, image recognition and constraint solving, process modeling and view integration, and visualization and animation tools.

Applications of Human Performance Models to System Design (Defense Research Series #2)

The human factors profession is currently attempting to take a more proactive role in the design of man-machine systems than has been character­ istic of its past. Realizing that human engineering contributions are needed well before the experimental evaluation of prototypes or operational systems, there is a concerted effort to develop tools that predict how humans will interact with proposed designs. This volume provides an over­ view of one category of such tools: mathematical models of human performance. It represents a collection of invited papers from a 1988 NATO Workshop. The Workshop was conceived and organized by NATO Research Study Group 9 (RSG.9) on "Modelling of Human Operator Behaviour in Weapon Systems". It represented the culmination of over five years of effort, and was attended by 139 persons from Europe, Canada, and the United States. RSG.9 was established in 1982 by Panel 8 of the Defence Research Group to accomplish the following objectives: * Determine the utility and state of the art of human performance modelling. * Encourage international research and the exchange of ideas. * Foster the practical application of modelling research. * Provide a bridge between the models and approaches adopted by engineers and behavioral scientists. * Present the findings in an international symposium.

Applications of Location Analysis (International Series in Operations Research & Management Science #232)

This book, companion to Foundations of Location Analysis (Springer, 2011), highlights some of the applications of location analysis within the spheres of businesses, those that deal with public services and applications that deal with law enforcement and first responders. While the Foundations book reviewed the theory and first contributions, this book describes how different location techniques have been used to solve real problems. Since many real problems comprise multiple objectives, in this book there is more presence of tools from multicriteria decision making and multiple-objective optimization.The section on business applications looks at such problems as locating bank branches, the potential location of a logistics park, sustainable forest management and layout problems in a hospital, a much more difficult type of problem than mere location problems.The section on public services presents chapters on the design of habitats for wildlife, control of forest fires, the location of intelligent sensors along highways for timely emergency response, locating breast cancer screening centers, an economic analysis for the locations of post offices and school location.The final section of the book includes chapters on the well-known problem of locating fire stations, a model for the location of sensors for travel time information, the problem of police districting, locations of jails, location of Coast Guard vessels and finally, a survey of military applications of location analysis throughout different periods of recent history.

Applications of Management Science (Applications of Management Science #20)

Applications of Management Science presents current studies in the application of management science to the solution of significant managerial decision-making problems. It significantly aids in the dissemination of the solution of managerial decision-making problems with management science methodologies. With a focus on the application of management science methodologies data envelopment analysis and multi-criteria decision making, this volume is split into three sections: 1. Applications of optimization; 2. Data envelopment analysis and applications; 3. Data envelopment analysis. To those involved in the applications of multi-criteria decision making, data envelopment analysis and decision making, in a realistic managerial problem solving environment through the use of state of the art management science modelling, this is a must-read.

Applications of Management Science (Applications of Management Science #21)

Applications of Management Science showcases current studies in the application of management science, contributing to the solution of significant managerial decision-making problems. To those involved in the applications of multi-criteria decision making, data envelopment analysis, and decision making, in a realistic managerial problem-solving environment through the use of state-of-the-art management science modeling, this is a must read. The research presented by academics in Volume 13 significantly aids in the deconstruction of managerial decision-making problems with management science methodologies. Specifically focusing on the applications of management science methodologies data envelopment analysis and multi-criteria decision making, this collection is split into three sections: Data Envelopment Analysis, Optimization Modeling, Business Analytical Modeling. Applications of Management Science is core for those academics, researchers, and practitioners of management science in mitigating significant managerial decision-making problems, for both the public and the private sectors.

Applications of Management Science (Applications of Management Science #18)

Applications of Management Science is a blind refereed refereed series. Each annual volume presents current studies in the application of management science to the solution of significant managerial decision-making problems. Authors investigate solutions to managerial decision-making problems using management science methodologies. In this volume, the first section is focused on multi-criteria decision applications, the second section on supply chain management and finally authors look at productivity analysis. Thus this volume will be of significant interest to those involved in the applications of these methods, in a realistic managerial problem solving environment through the use of state of the art management science modeling.

Applications of Management Science: Network Optimization In Applications (Applications of Management Science #18)

Applications of Management Science is a blind refereed refereed series. Each annual volume presents current studies in the application of management science to the solution of significant managerial decision-making problems. Authors investigate solutions to managerial decision-making problems using management science methodologies. In this volume, the first section is focused on multi-criteria decision applications, the second section on supply chain management and finally authors look at productivity analysis. Thus this volume will be of significant interest to those involved in the applications of these methods, in a realistic managerial problem solving environment through the use of state of the art management science modeling.

Applications of Management Science (Applications of Management Science #20)

Applications of Management Science presents current studies in the application of management science to the solution of significant managerial decision-making problems. It significantly aids in the dissemination of the solution of managerial decision-making problems with management science methodologies. With a focus on the application of management science methodologies data envelopment analysis and multi-criteria decision making, this volume is split into three sections: 1. Applications of optimization; 2. Data envelopment analysis and applications; 3. Data envelopment analysis. To those involved in the applications of multi-criteria decision making, data envelopment analysis and decision making, in a realistic managerial problem solving environment through the use of state of the art management science modelling, this is a must-read.

Applications of Management Science (Applications of Management Science #21)

Applications of Management Science showcases current studies in the application of management science, contributing to the solution of significant managerial decision-making problems. To those involved in the applications of multi-criteria decision making, data envelopment analysis, and decision making, in a realistic managerial problem-solving environment through the use of state-of-the-art management science modeling, this is a must read. The research presented by academics in Volume 13 significantly aids in the deconstruction of managerial decision-making problems with management science methodologies. Specifically focusing on the applications of management science methodologies data envelopment analysis and multi-criteria decision making, this collection is split into three sections: Data Envelopment Analysis, Optimization Modeling, Business Analytical Modeling. Applications of Management Science is core for those academics, researchers, and practitioners of management science in mitigating significant managerial decision-making problems, for both the public and the private sectors.