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Elsevier

Books in Data structures

  • Data Compression for Data Mining Algorithms

    • 1st Edition
    • 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.

  • Foundations of Cloud Computing

    • 1st Edition
    • 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
    • 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
    • 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.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1st Edition
    • 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
    • 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.

  • Advanced Topics in Inverse Data Envelopment Analysis

    Approaches for Handling Ratio Data
    • 1st Edition
    • Mehdi Soltanifar + 3 more
    • English
    Advanced Topics in Inverse Data Envelopment Analysis: Approaches for Handling Ratio Data explores and tackles the most significant challenges encountered by researchers and practitioners in decision analysis and performance evaluation. This book delves into the sophisticated realm of Ratio Data Envelopment Analysis (DEA-R), offering a thorough examination of advanced methodologies, practical examples, and insights into managing complex problems involving both non-negative and negative data. Filling crucial gaps in existing literature, this comprehensive resource focuses on the emerging field of Inverse DEA-R, equipping readers with the necessary tools and knowledge to address a wide range of challenging data types. This book serves as an essential guide for making informed and efficient decisions, guiding researchers and graduate students in computer science, applied mathematics, industrial engineering, and finance, navigating the complexities of decision analysis in today's data-driven world.

  • Applied Graph Data Science

    Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
    • 1st Edition
    • Pethuru Raj + 4 more
    • English
    Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

  • Computational Intelligence for Genomics Data

    • 1st Edition
    • Babita Pandey + 4 more
    • English
    Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

  • Data Science in the Medical Field

    • 1st Edition
    • Seifedine Kadry + 1 more
    • English
    Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

  • Synthetic Data and Generative AI

    • 1st Edition
    • Vincent Granville
    • English
    Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

  • Data Analytics for Social Microblogging Platforms

    • 1st Edition
    • Soumi Dutta + 3 more
    • English
    Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

  • Data Simplification

    Taming Information With Open Source Tools
    • 1st Edition
    • Jules J. Berman
    • English
    Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

  • Mobile Data Loss

    Threats and Countermeasures
    • 1st Edition
    • Michael T. Raggo
    • English
    The mobile threat landscape is evolving bringing about new forms of data loss. No longer can organizations rely on security policies designed during the PC era. Mobile is different and therefore requires a revised approach to countermeasures to mitigate data loss. Understanding these differences is fundamental to creating a new defense-in-depth strategy designed for mobile. Mobile Data Loss: Threats & Countermeasures reviews the mobile threat landscape using a hacker mind-set to outline risks and attack vectors that include malware, risky apps, operating system compromises, network attacks, and user behaviours. This provides the basis for then outlining countermeasures for defining a holistic mobile security methodology that encompasses proactive protections, response mechanisms, live monitoring, and incident response. Designing a comprehensive mobile security strategy is key. Mobile Data Loss: Threats & Countermeasures outlines the threats and strategies for protecting devices from a plethora of data loss vectors.

  • Data Mapping for Data Warehouse Design

    • 1st Edition
    • Qamar Shahbaz
    • English
    Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.

  • Repurposing Legacy Data

    Innovative Case Studies
    • 1st Edition
    • Jules J. Berman
    • English
    Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data—data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

  • Principles of Artificial Intelligence

    • 1st Edition
    • Nils J. Nilsson
    • English
    A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

  • Architecture and Patterns for IT Service Management, Resource Planning, and Governance

    Making Shoes for the Cobbler's Children
    • 2nd Edition
    • Charles T. Betz
    • English
    Information technology supports efficient operations, enterprise integration, and seamless value delivery, yet itself is too often inefficient, un-integrated, and of unclear value. This completely rewritten version of the bestselling Architecture and Patterns for IT Service Management, Resource Planning and Governance retains the original (and still unique) approach: apply the discipline of enterprise architecture to the business of large scale IT management itself. Author Charles Betz applies his deep practitioner experience to a critical reading of ITIL 2011, COBIT version 4, the CMMI suite, the IT portfolio management literature, and the Agile/Lean IT convergence, and derives a value stream analysis, IT semantic model, and enabling systems architecture (covering current topics such as CMDB/CMS, Service Catalog, and IT Portfolio Management). Using the concept of design patterns, the book then presents dozens of visual models documenting challenging problems in integrating IT management, showing how process, data, and IT management systems must work together to enable IT and its business partners. The edition retains the fundamental discipline of traceable process, data, and system analysis that has made the first edition a favored desk reference for IT process analysts around the world. This best seller is a must read for anyone charged with enterprise architecture, IT planning, or IT governance and management.

  • Pricing, Risk, and Performance Measurement in Practice

    The Building Block Approach to Modeling Instruments and Portfolios
    • 1st Edition
    • Wolfgang Schwerdt + 1 more
    • English
    How can managers increase their ability to calculate price and risk data for financial instruments while decreasing their dependence on a myriad of specific instrument variants? Wolfgang Schwerdt and Marcelle von Wendland created a simple and consistent way to handle and process large amounts of complex financial data. By means of a practical framework, their approach analyzes market and credit risk exposure of financial instruments and portfolios and calculates risk adjusted performance measures. Its emphasis on standardization yields significant improvements in speed and accuracy.Schwerdt and von Wendland's focus on practical implementation directly addresses limitations imposed by the complex and costly processing time required for advanced risk management models and pricing hundreds of thousands of securities each day. Their many examples and programming codes demonstrate how to use standards to build financial instruments, how to price them, and how to measure the risk and performance of the portfolios that include them.

  • Foundations of Multidimensional and Metric Data Structures

    • 1st Edition
    • Hanan Samet
    • English
    Foundations of Multidimensional and Metric Data Structures provides a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Each section includes a large number of exercises and solutions to self-test and confirm the reader's understanding and suggest future directions. The book is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programming, image processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design.

  • OpenVMS Alpha Internals and Data Structures

    Memory Management
    • 1st Edition
    • Ruth Goldenberg
    • English
    OpenVMS Alpha Internals and Data Structures: Memory Management is an updateto selected parts of the book OpenVMS AXP Internals and Data Structures Version 1.5 (Digital Press, 1994). This book covers the extensions to the memory management subsystem of OpenVMS Alpha to allow the operating system and applications to access 64 bits of address space. It emphasizes system data structures and their manipulation by paging and swapping routines and related system services.It also describes management of dynamic memory, such as nonpaged pool, and support for nonuniform memory access (NUMA) platforms.This book is intended for systems programmers, technical consultants, application designers, and other computer progressions interested in learning the details of the OpenVMS executive. Teachers and students of graduate and advanced undergraduate courses in operating systems will find this book a valuable study in how theory and practice are resolved in a complex commercialoperating system.

  • Component Database Systems

    • 1st Edition
    • Klaus R. Dittrich + 1 more
    • English
    Component Database Systems is a collection of invited chapters by the researchers making the most influential contributions in the database industry's trend toward componentizationThis book represents the sometimes-divergent, sometimes-convergent approaches taken by leading database vendors as they seek to establish commercially viable componentization strategies. Together, these contributions form the first book devoted entirely to the technical and architectural design of component-based database systems. In addition to detailing the current state of their research, the authors also take up many of the issues affecting the likely future directions of component databases.If you have a stake in the evolution of any of today's leading database systems, this book will make fascinating reading. It will also help prepare you for the technology that is likely to become widely available over the next several years.

  • Distributed Information Resources

    • 1st Edition
    • Volume 48
    • English
    Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in hardware and software and in computer theory, design, and applications. It has also provided contributors with a medium in which they can examine their subjects in greater depth and breadth than that allowed by standard journal articles. As a result, many articles have become standard references that continue to be of significant, lasting value despite the rapid growth taking place in the field.