Название: Comparative Efficiency in Data Envelopment Analysis Based on Ratio Analysis
Автор: Fаrhаd Ноssеinzаdеh Lоtfi, Тоfigh Аllаhvirаnlоо, Witоld Реdrусz
Издательство: Springer
Серия: Studies in Big Data
Год: 2023
Страниц: 192
Язык: английский
Формат: pdf (true), epub
Размер: 19.0 MB

Data envelopment analysis (DEA) is a useful tool for evaluating the performance of homogeneous DMUs, which has been the focus of researchers in recent years. On the other hand, DEA-R is a combination of DEA and Ratio Analysis, which is a hybrid technique for calculating efficiency, ranking DMUs, and finding efficient faces. Therefore, the relationship between DEA and DEA-R, as well as the study of ratio analysis and the difference between the concepts of DEA-R and DEA with ratio data, can provide a suitable field for researchers in the field of evaluating the performance of DMUs. Requirement of the present book is to study the basic concepts of data envelopment analysis, and the chapters of this book are the development of basic DEA models in DEA-R and ratio analysis, which can be used for researchers in the fields of applied mathematics, industrial engineering, management and computer engineering. This book is the effort of the authors’ group in the field of research and implementation of practical projects over many years.

Название: Big Data Analytics for Human-Computer Interactions: A New Era of Computation
Автор: Kuldeep Singh Kaswan, Anupam Baliyan, Jagjit Singh Dhatterwal
Издательство: Bentham Books
Серия: IoT and Big Data Analytics (Volume 3)
Год: 2023
Страниц: 392
Язык: английский
Формат: pdf (true), epub
Размер: 25.8 MB

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics is a significant challenge in developing highly scalable data and data integration algorithms. New algorithms, methods, systems, and applications in Big Data Analytics are potential discoveries that will effectively identify valuable and hidden information in Big Data. This chapter discusses big data, and its history; Big Data drives the world's modern organizations. There is a need to convert Big Data into Business Intelligence that enterprises can readily deploy. Better data leads to better decision-making and improved strategies for organizations. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems.

Название: Data Analytics for Internet of Things Infrastructure
Автор: Rohit Sharma, Gwanggil Jeon, Yan Zhang
Издательство: Springer
Серия: Internet of Things
Год: 2023
Страниц: 330
Язык: английский
Формат: pdf (true), epub
Размер: 50.0 MB

This book provides techniques for the deployment of semantic technologies in data analysis along with the latest applications across the field such as Internet of Things (IoT). The authors focus on the use of the IoT and big data in business intelligence, data management, Hadoop, Machine Learning, cloud, smart cities, etc. They discuss how the generation of Big Data by IoT has ruptured the existing data processing capacity of IoT and recommends the adoption of data analytics to strengthen solutions. The book addresses the challenges in designing the web based IoT system, provides a comparative analysis of different advanced approaches in industries, and contains an analysis of databases to provide expert systems. The book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of IoT and Big Data analytics.

Название: Statistics and Data Science for Teachers
Автор: Anna Bargagliotti, Christine Franklin
Издательство: American Statistical Association
Год: 2021
Страниц: 248
Язык: английский
Формат: pdf
Размер: 34.9 MB

A main goal of Statistics and Data Science for Teachers is to provide teacher educators with a resource to guide entire courses and professional development, or portions of courses and professional development when preparing teachers of all school level grade levels to teach the foundations of statistics and data science in their classrooms. In supporting the spirit of Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education, this book presents statistical ideas through investigations and engagement with the statistical problem-solving process of formulating statistical investigative questions, collecting/considering data, analyzing data, and interpreting results. The book is organized into three sections: 1) “Statistics as a Problem-Solving Process”; 2) “Toward Data Science”; 3) “Probability Unpacked”. Within each of these sections, standard topics of descriptive statistics, associations and relationships, distributions, probability, and sampling distributions are all developed.

Название: Big Data Concepts, Technologies, and Applications
Автор: Mohd. Shahid Husain, Mohammad Zunnun Khan, Tamanna Siddiqui
Издательство: CRC Press
Год: 2024
Страниц: 216
Язык: английский
Формат: pdf (true)
Размер: 45.0 MB

With the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them. Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of Big Data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussion about the important sectors where big data analytics has proven to be very effective in improving performance and helping industries to remain competitive. This book provides insight into the novel areas of big data analytics and the research directions for the scholars working in the domain.

Название: Demystifying Big Data Analytics for Industries and Smart Societies
Автор: Keshav Kaushik, Mamta Dahiya, Ashutosh Dhar Dwivedi
Издательство: CRC Press
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf (true)
Размер: 12.3 MB

This book aims to provide readers with a comprehensive guide to the fundamentals of Big Data analytics and its applications in various industries and smart societies. What sets this book apart is its in-depth coverage of different aspects of Big Data analytics, including Machine Learning algorithms, spatial data analytics, and IoT-based smart systems for precision agriculture. The book also delves into the use of Big Data analytics in healthcare, energy management, and agricultural development, among others. The authors have used clear and concise language, along with relevant examples and case studies, to help readers understand the complex concepts involved in Big Data analytics. In today’s digital age, data has become the most valuable asset for businesses and societies. The increasing digitization of processes and the rise of the Internet of Things (IoT) have led to an exponential growth in the amount of data being generated. This data holds valuable insights that can help businesses make better decisions, optimize processes, and improve customer experiences. However, managing and analyzing this massive amount of data can be overwhelming, especially for those who are new to the field of Big Data analytics.

Название: Service-Oriented Computing and System Integration: Software, IoT, Big Data, and AI as Services 8th Edition
Автор: Yinong Chen, Gennaro De Luca
Издательство: Kendall Hunt Publishing
Год: 2022
Страниц: 806
Язык: английский
Формат: pdf
Размер: 75.8 MB

New Eighth Edition Now Available! Service-Oriented Computing and System Integration: Software, IoT, Big Data, and AI as Services focuses on service-oriented computing, web application development, and service-oriented system integration. It covers WSDL services, RESTful services, their development and applications, XML and related technologies, Web data management and database accesses, architecture-driven approach, workflow-based software integration, device and IoT integration, as well as the latest technologies in Big Data processing, cloud computing, Machine Learning, Artificial Intelligence, and cross-platform mobile/phone application development. The book teaches both principles and hands-on programming skills, leading to operational software development. Exercises, practice questions, and projects are provided at the end of each chapter to help students review and apply what they have learned. We assume tile readers arc familiar with the object-oriented (OO) computing concepts and have used one of the OO programming languages such as C++, Java, or C# in writing OO programs. However, we start the chapter with a brief introduction to C# and .Net in urder to prepare the readers who have not used C#. Then, the chapter moves to the general issues in multitasking and multithreading, including parallel processing, synchronisation, deadlock, and order of executions, which form the foundation of a distributed computing paradigm.

Название: Value-Driven data: Identifying, Communicating and Delivering Effective Business Solutions with Data
Автор: Edosa Odaro
Издательство: Kogan Page
Год: 2023
Страниц: 257
Язык: английский
Формат: pdf (true), epub
Размер: 14.0 MB

Value-Driven Data explains how data and business leaders can co-create and deploy data-driven solutions for their organizations. Value-Driven Data explores how organizations can understand their problems and come up with better solutions, aligning data storytelling with business needs. The book reviews the main challenges that plague most data-to-business interactions and offers actionable strategies for effective data value implementation, including methods for tackling obstacles and incentivizing change. Value-Driven Data is supported by tried-and-tested frameworks that can be applied to different contexts and organizations. It features cutting-edge examples relating to digital transformation, data strategy, resolving conflicts of interests, building a data P&L and AI value prediction methodology. Recognizing different types of data value, this book presents tangible methodologies for identifying, capturing, communicating, measuring and deploying data-enabled opportunities. This is essential reading for data specialists, business stakeholders and leaders involved in capturing and executing data value opportunities for organizations and for informing data value strategies. But what is data value? And why is it so important? Put simply, I refer to data value as the potential for data to create economic opportunities, drive beneficial actions and improve our life outcomes in countless ways. With the help of advanced technology like Artificial Intelligence (AI) and Machine Learning, we can now process vast amounts of data to enable us to uncover hidden patterns, predict plausible possibilities and make better decisions than ever before.

Название: Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Cataloging, Governance, Integration, Orchestration, and Consumption
Автор: Eberhard Hechler, Maryela Weihrauch, Yan (Catherine) Wu
Издательство: Apress
Год: 2023
Страниц: 440
Язык: английский
Формат: pdf (true), epub
Размер: 41.6 MB

Understand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance—all designed to deliver "data as a product" within hybrid cloud landscapes. This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses Artificial Intelligence (AI) and Machine Learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.

Название: The Way of data: From Technology to Applications
Автор: Wu Hequan, Zheng Weimin
Издательство: World Scientific Publishing
Год: 2023
Страниц: 200
Язык: английский
Формат: pdf (true)
Размер: 18.9 MB

Everyone is already in the era of Big Data. It is gradually changing people's lifestyles. Therefore, it is necessary to explore the development path of Big Data, balance the relationship between technology, policy, and the market, so that it can better serve human society.This comprehensive book introduces what Big Data is, Big Data processing systems, Big Data management technologies, and Big Data analysis methods in an easy-to-understand language. It explains the specific applications of Big Data in smart government affairs, economic development, and the improvement of people's livelihood and welfare. Big Data, as described by Gartner, the world’s leading IT research and advisory firm, is a massive, high-growth, and diverse information asset that requires new processing models to provide greater decision-making, insight discovery, and process optimization.