Fundamentals of Database Systems (7版)

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書名：Fundamentals of Database Systems, 7E Global Edition 作者：ELMASRI & NAVATHE 出版社：PEARSON 出版日期：2017/00/00 ISBN：9781292097619 Table of Contents: Part 1: Introduction to Databases Chapter 1: Databases and Database Users Chapter 2: Database Systems Concepts and Architecture Part 2: Conceptual Data Modeling and Database Design Chapter 3: Data Modeling Using the Entity Relationship (ER) Model Chapter 4: The Enhanced Entity Relationship (EER) Model Part 3: The Relational Data Model and SQL Chapter 5: The Relational Data Model and Relational Database Constraints Chapter 6: Basic SQL Chapter 7: More SQL: Complex Queries, Triggers, Views, and Schema Modification Chapter 8: The Relational Algebra and Relational Calculus Chapter 9: Relational Database Design by ER- and EER-to-Relational Mapping Part 4: Database Programming Techniques Chapter 10: Introduction to SQL Programming Techniques Chapter 11: Web Database Programming Using PHP Part 5: Object, Object-Relational, and XML: Concepts, Models, Languages, and Standards Chapter 12: Object and Object-Relational Databases Chapter 13: XLM: Extensible Markup Language Part 6: Database Design Theory and Normalization Chapter 14: Basics of Functional Dependencies and Normalization for Relational Databases Chapter 15: Relational Database Design Algorithms and Further Dependencies Part 7: File Structures, Hashing, Indexing, and Physical Database Design Chapter 16: Disc Storage, Basic File Structures, Hashing, and Modern Storage Architectures Chapter 17: Indexing Structures for Files and Physical Database Design Part 8: Query Processing and Optimization Chapter 18: Strategies for Query Processing Chapter 19: Query Optimization Part 9: Transaction Processing, Concurrency Control, and Recovering Chapter 20: Introduction to Transaction Processing Concepts and Theory Chapter 21: Concurrency Control Techniques Chapter 22: Database Recovery Techniques Part 10: Distributed Databases, NOSQL Systems, Cloud Computing, and Big Data Chapter 23: Distributed Database Concepts Chapter 24: NOSQL Databases and Big Data Storage Systems Chapter 25: Big Data Technologies Based on MapReduce and Hadoop Part 11: Advanced Database Models, Systems, and Applications Chapter 26: Enhanced Data Models: Introduction to Active, Temporal, Spatial, Multimedia, and Deductive Databases Chapter 27: Introduction to Information Retrieval and Web Search Chapter 28: Data Mining Concepts Chapter 29: Overview of Data Warehousing and OLAP Part 12: Additional Database Topics: Security Chapter 30: Database Security Appendix A: Alternative Diagrammatic Notations for ER Models Appendix B: Parameters of Disks Appendix C: Overview of the QBE Language Appendix D: Overview of the Hierarchical Data Model Appendix E: Overview of the Network Data Model

Fundamentals of Cloud Security (1版)

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【簡介】 Fundamentals of Cloud Security offers a structured, framework-based approach to understanding, implementing, and securing cloud environments. Built around the NIST Cybersecurity Framework, it connects identification, protection, detection, response, and recovery to practical applications across AWS, Azure, and Google Cloud. This First Edition text guides readers through readiness assessments, shared responsibility, governance, compliance, and architectural considerations that shape secure, scalable cloud systems. Content explores the cloud threat ecosystem, principles of security and trust, and the strategies, tools, and controls that drive resilient operations. Case studies and real-world examples illustrate how to bridge theory and practice, while coverage of emerging security roles, automated tools, and AI-driven techniques highlights the evolution of cloud protection. Designed to align with cybersecurity and cloud computing programs, it supports modern learning outcomes and prepares learners to address current and future challenges in the field. Features and Benefits Organized around the NIST Cybersecurity Framework, linking key lifecycle stages to practical cloud security strategies. Applies real-world scenarios and provider specifics for AWS, Azure, and Google Cloud to strengthen practical understanding. Covers readiness, governance, and compliance, providing tools to evaluate cloud migration and manage ongoing alignment. Highlights emerging roles and shift-left/shift-right security, guiding modern approaches to multi-cloud threat management. Explores AI-driven and automated security approaches that enhance monitoring, response, and governance across deployments. Offers immersive, scenario-based Cloud Labs that reinforce concepts through real-world, hands-on virtual practice. Instructor resources include PowerPoint slides, test bank, sample syllabi, instructor manual, and time-on-task documentation.

Fundamentals of Probability (5版)

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【簡介】 This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. 【目錄】 1. Axioms of Probability 2. Combinatorial Methods 3. Conditional Probability and Independence 4. Distribution Functions and Discrete Random Variables 5. Continuous Random Variables 6. Special Discrete Distributions 7. Special Continuous Distributions 8. Understanding Relationships: Covariance, Correlations, and Conditional Distributions 9. More Bivariate and Multivariate Topics 10. Inequalities and Limit Theorems Appendix Tables

Fundamentals of Probability and Statistics for Machine Learning (1版)

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【簡介】 An introductory textbook for undergraduate or beginning graduate students that integrates probability and statistics with their applications in machine learning.Most curricula have students take an undergraduate course on probability and statistics before turning to machine learning. In this innovative textbook, Ethem Alpaydın offers an alternative tack by integrating these subjects for a first course on learning from data. Alpaydın accessibly connects machine learning to its roots in probability and statistics, starting with the basics of random experiments and probabilities and eventually moving to complex topics such as artificial neural networks. With a practical emphasis and learn-by-doing approach, this unique text offers comprehensive coverage of the elements fundamental to an empirical understanding of machine learning in a data science context. Consolidates foundational knowledge and key techniques needed for modern data scienceCovers mathematical fundamentals of probability and statistics and ML basicsEmphasizes hands-on learningSuits undergraduates as well as self-learners with basic programming experienceIncludes slides, solutions, and code