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Books in Information systems

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1-10 of 254 results in All results

Professional Penetration Testing

  • 3rd Edition
  • January 1, 2025
  • Thomas Wilhelm
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 4 7 8 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 4 7 9 - 5
Professional Penetration Testing: Creating and Learning in a Hacking Lab, Third Edition walks the reader through the entire process of setting up and running a pen test lab. Penetration testing—the act of testing a computer network to find security vulnerabilities before they are maliciously exploited—is a crucial component of information security in any organization. Chapters cover planning, metrics, and methodologies, the details of running a pen test, including identifying and verifying vulnerabilities, and archiving, reporting and management practices. The material presented will be useful to beginners through advanced practitioners.Here, author Thomas Wilhelm has delivered penetration testing training to countless security professionals, and now through the pages of this book, the reader can benefit from his years of experience as a professional penetration tester and educator. After reading this book, the reader will be able to create a personal penetration test lab that can deal with real-world vulnerability scenarios. "...this is a detailed and thorough examination of both the technicalities and the business of pen-testing, and an excellent starting point for anyone getting into the field." –Network Security

Mathematical Modeling for Big Data Analytics

  • 1st Edition
  • January 1, 2025
  • 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 1, 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/organizational 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/company 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.

Digital Universalism and Cultural Diversity

  • 1st Edition
  • December 1, 2019
  • Laurence Favier
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 0 7 1 - 6
Digital Universalism and Cultural Diversity details the concept of digital universalism as a wonderful horizon of an interconnected planet (men and objects) and the expression of a cultural hegemony that formats cultural diversity. It presents a few essential directions that are at the core of the debate between digital universalism and cultural diversity and the future perspectives to fathom. As the smartphone and its interfaces are at the center of most of our activities, it is important that we understand our behaviors and how they fuel connected digital devices. This book tackles these questions in the rapidly moving digital era.

Handbook of Probabilistic Models

  • 1st Edition
  • October 5, 2019
  • Pijush Samui + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 6 5 1 4 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 6 5 4 6 - 1
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.

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.

From Digital Traces to Algorithmic Projections

  • 1st Edition
  • September 7, 2018
  • Thierry Berthier + 1 more
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 2 7 0 - 0
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 2 3 6 9 - 3
From Digital Traces to Algorithmic Projections describes individual digital fingerprints in interaction with the different algorithms they encounter throughout life. Centered on the human user, this formalism makes it possible to distinguish the voluntary projections of an individual and their systemic projections (suffered, metadata), both open (public) and closed. As the global algorithmic projection of an individual is now the focus of attention (Big Data, neuromarketing, targeted advertising, sentiment analysis, cybermonitoring, etc.) and is used to define new concepts, this resource discusses the ubiquity of place and the algorithmic consent of a user.

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