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Books in Database management

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    • Mastering Cloud Computing

      • 2nd Edition
      • March 1, 2026
      • Rajkumar Buyya + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 0 4 3 5 1
      • eBook
        9 7 8 0 4 4 3 4 0 4 3 6 8
      Mastering Cloud Computing: Foundations and Applications Programming, Second Edition is designed for readers learning to develop cloud computing applications. Today’s applications don’t live on a single computer but are deployed from and reside on a virtual server, accessible anywhere, any time. Today’s application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. The book covers all you need to develop applications for cloud architectures, including industrial platforms and security considerations. Real-world case studies and examples demonstrate the key concepts, with exercises and labs throughout the book.

    • Mathematical Modeling for Big Data Analytics

      • 1st Edition
      • November 1, 2025
      • Passent El-Kafrawy + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 6 7 3 5 2
      • eBook
        9 7 8 0 4 4 3 2 6 7 3 6 9
      Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

    • The Essential Criteria of Graph Databases

      • 1st Edition
      • January 16, 2024
      • Ricky Sun
      • English
      • Paperback
        9 7 8 0 4 4 3 1 4 1 6 2 1
      • eBook
        9 7 8 0 4 4 3 1 4 1 6 3 8
      The Essential Criteria of Graph Databases collects several truly innovative graph applications in asset-liability and liquidity risk management to spark readers’ interest and further broaden the reach and applicable domains of graph systems. Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability, 2. Silos, slews of siloed systems across the AI ecosystem, 3. Low-performance, as most of ML/DL based AI systems are SLOW. Hence, fixing these problems paves the road to strong and effective AI.

    • Machine Learning

      • 2nd Edition
      • March 1, 2023
      • Marco Gori + 2 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 9 8 5 9 1
      • eBook
        9 7 8 0 3 2 3 9 8 4 6 9 0
      Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.

    • Classification Made Relevant

      • 1st Edition
      • January 25, 2022
      • Jules J. Berman
      • English
      • Paperback
        9 7 8 0 3 2 3 9 1 7 8 6 5
      • eBook
        9 7 8 0 3 2 3 9 7 2 5 8 1
      Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences. The book's chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.

    • Data Stewardship

      • 2nd Edition
      • October 31, 2020
      • David Plotkin
      • English
      • Paperback
        9 7 8 0 1 2 8 2 2 1 3 2 7
      • eBook
        9 7 8 0 1 2 8 2 2 1 6 7 9
      Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling "big data" and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data—moving from business/organizatio... function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered.Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/compa... structure, business functions, and data ownership. The book shows data managers how to gain support for a stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards.

    • Data Architecture: A Primer for the Data Scientist

      • 2nd Edition
      • April 30, 2019
      • W.H. Inmon + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 6 9 1 6 2
      • eBook
        9 7 8 0 1 2 8 1 6 9 1 7 9
      Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

    • Big Data Analytics for Sensor-Network Collected Intelligence

      • 1st Edition
      • February 2, 2017
      • Hui-Huang Hsu + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 9 3 9 3 1
      • eBook
        9 7 8 0 1 2 8 0 9 6 2 5 3
      Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS

    • Digital Libraries

      • 1st Edition
      • January 21, 2016
      • Fabrice Papy
      • English
      • Hardback
        9 7 8 1 7 8 5 4 8 0 4 5 4
      • eBook
        9 7 8 0 0 8 1 0 0 4 8 6 9
      The technological interoperability of digital libraries must be rethought in order to adapt to new uses and networks. Informative digital environments aimed at responding to heritage, cultural, scientific or commercial demands have taken over the global cyberspace and have redesigned the techno-informative landscape of the Web. However, while the technological models demonstrate their effectiveness and explain to a large extent the creation of digital libraries, archives and deposits, the subjacent concept of uses continues to cause debate. The information technologies used by heterogeneous digital libraries enable a technical interoperability of content. This is not enough to allow the adhesion of a public connected to very different information profiles and techniques. This book explores the avenues of a user-orientated interoperability where the questions of consultation interfaces and content description processes are studied.

    • Temporal Data Mining via Unsupervised Ensemble Learning

      • 1st Edition
      • November 15, 2016
      • Yun Yang
      • English
      • Paperback
        9 7 8 0 1 2 8 1 1 6 5 4 8
      • eBook
        9 7 8 0 1 2 8 1 1 8 4 1 2
      Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.