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Books in Data

This collection spans data management, storage, retrieval, and big data analytics. Showcasing advanced database technologies, data science, and data mining, it supports researchers, data scientists, and industry professionals in harnessing data for insights. Addressing data quality, privacy, and scalable architectures, these resources enable informed decision-making and foster innovation in data-driven domains.

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • August 1, 2026
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are thoroughly explained through real-world and publicly available data sets. R codes are presented in a manner that helps readers understand the program code syntax. Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world data sets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Data Compression for Data Mining Algorithms

    • 1st Edition
    • May 1, 2026
    • Xiaochun Wang
    • English
    Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

  • Digital Twins for Sustainable Development

    • 1st Edition
    • April 1, 2026
    • Valentina Emilia Balas + 4 more
    • English
    Digital Twins for Sustainable Development covers digital twins for sustainability as a virtual representation of a physical system or environment, such as a building, city, or natural ecosystem and how they are used to support sustainable development and management practices. The book demonstrates how data from a variety of sources, such as sensors, satellite imagery, and other monitoring tools can be used for advanced analytics and modeling techniques to simulate the system's behavior over time. This allows researchers and professionals in computer science to manage complex systems and promote sustainable development and resource management practices.

  • Quantum Cryptography and Annealing for Securing Industrial IoT

    • 1st Edition
    • March 6, 2026
    • Seifedine Kadry + 5 more
    • English
    Quantum Cryptography and Annealing for Securing Industrial IoT focuses on the rapidly evolving field of quantum security solutions for Industrial Internet of Things (IIoT) platforms, emphasizing the critical intersection of quantum cryptography, post-quantum cryptography, and their practical applications in IIoT. The book’s primary objective is to drive advancements that significantly intersect quantum cryptography in securing IIoT devices, elevate secure IIoT infrastructures, and optimize the overall delivery. Distinguishing itself by prioritizing practical applications, it offers a nuanced perspective on how technological integrations in quantum cryptosystems are actively employed in real-world scenarios. The authors meticulously examine the role of quantum cryptosystems in the design, analysis, and optimization of IIoT-specific hardware, covering their resilience to physical and side-channel attacks and evaluating performance. This book strikes a balance between theoretical concepts and practical applications, providing insights into the challenges and solutions encountered in applying quantum cryptographical principles to IIoT engineering problems.

  • Foundations of Cloud Computing

    • 1st Edition
    • February 15, 2026
    • Robert Shimonski
    • English
    Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

  • Quantum Theory, Decision Making and Social Dynamics

    • 1st Edition
    • January 8, 2026
    • Tofigh Allahviranloo + 3 more
    • English
    Quantum Theory, Decision Making, and Social Dynamics is a detailed exploration of the connection between quantum theory, decision-making, and social networks. As quantum theory expands into various fields, there is an increasing demand for accessible resources that clarify its principles and uses. This book aims to address that need by explaining the complex relationship between quantum theory and social dynamics, especially in decision-making contexts. It discusses the challenges of understanding and applying quantum theory in social settings and provides readers with the knowledge to leverage its potential in decision-making processes. The book is divided into eleven chapters, each focusing on a specific aspect of quantum theory and its applications. Chapter 1 introduces quantum theory, fuzzy logic, and social network analysis, highlighting key concepts like superposition, entanglement, and fuzzy influence within networks. Chapter 2 examines fuzzy sets, membership functions, and inference systems, with applications in devices, traffic management, and healthcare. Chapter 3 covers the mathematical framework of quantum mechanics and its philosophical paradoxes, connecting them to fuzzy logic models of uncertainty. Chapter 4 links social networks to quantum graphs, defining their topology, centrality, and entangled edges. Chapter 5 models social identity as a fuzzy quantum superposition, exploring identity collapse and coherence within networks. Chapter 6 relates quantum entanglement to social ties, proposing fuzzy–quantum graph models for interconnected systems. Chapter 7 analyses measures of irregularity in quantum graphs and applies these to financial networks. Chapter 8 integrates quantum cognition with fuzzy MCDM, employing various probability evaluation methods. Chapter 9 features case studies of fuzzy systems and their integration with quantum fuzzy graphs. Chapter 10 develops a quantum graph-based link prediction model for dynamic social networks. Chapter 11 concludes with a summary of the quantum–fuzzy framework, discussing its contributions, limitations, and future directions.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1st Edition
    • January 6, 2026
    • Pallavi Chavan + 2 more
    • English
    Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

  • Multimodal Learning Using Heterogeneous Data

    • 1st Edition
    • December 15, 2025
    • Saeid Eslamian + 3 more
    • English
    Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1st Edition
    • July 30, 2025
    • Harris V. Georgiou
    • English
    Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

  • Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

    • 1st Edition
    • July 24, 2025
    • Tilottama Goswami + 2 more
    • English
    Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.

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