The third edition of Database Principles maintains its engaging writing style and brevity; its unique balance between theory and practice; and its wealth of examples throughout the text. It provides a solid and practical foundation for the design, implementation and management of database systems. The new edition has been updated with all the latest developments and technologies and incorporates a generous number of localised and motivating business vignettes that tie the concepts to real-life situations. The coverage of SQL provides numerous examples and simpler explanations that focus on areas most important for a database career. This edition is suitable for a first course in databases at undergraduate level and will also provide essential material for conversion postgraduate courses. Providing comprehensive and practical coverage of core database concepts, it is an ideal text not only for those studying database management systems in the context of computer science, but also those on courses in the areas of information systems and business information technology.

Equip your students with a management-focused overview of information security as well as the tools to effectively administer it with Whitman/Mattord's MANAGEMENT OF INFORMATION SECURITY, Sixth Edition.

More than ever, we need to prepare information security management students to build and staff security programs capable of securing systems and networks to meet the challenges in a world where continuously emerging threats, ever-present attacks and the success of criminals illustrate weaknesses in current information technologies. This text offers an exceptional blend of skills and experiences to administer and manage the more secure computing environments that organizations need.

Reflecting the latest developments from the field, it includes updated coverage of NIST, ISO and security governance along with emerging concerns like Ransomware, Cloud Computing and the Internet of Things.

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.

Safety of Web Applications: Risks, Encryption and Handling Vulnerabilities with PHP explores many areas that can help computer science students and developers integrate security into their applications. The Internet is not secure, but it's very friendly as a tool for storing and manipulating data. Customer confidence in Internet software is based on it's ability to prevent damage and attacks, but secure software is complicated, depending on several factors, including good risk estimation, good code architecture, cyphering, web server configuration, coding to prevent the most common attacks, and identification and rights allocation.

Big Data and Smart Service Systems presents the theories and applications regarding Big Data and smart service systems, data acquisition, smart cities, business decision-making support, and smart service design. The rapid development of computer and Internet technologies has led the world to the era of Big Data. Big Data technologies are widely used, which has brought unprecedented impacts on traditional industries and lifestyle. More and more governments, business sectors, and institutions begin to realize data is becoming the most valuable asset and its analysis is becoming the core competitiveness.

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.

The originality of this book, which deals with such a new subject matter, lies in the application of methods and concepts never used before - such as ontologies and taxonomies, as well as thesauri - to the ordering of knowledge based on primary information. Chapters in the book also examine the study of ontologies, taxonomies and thesauri from the perspective of systematics and general systems theory. Ontologies, Taxonomies and Thesauri in Systems Science and Systematics will be extremely useful to those operating within the network of related fields, which includes documentation and information science.
Ontologies and TaxonomyKnowledge classification SystemsThesauri

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.
Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.
Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data miningA valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

Almost every organization seeks a simple means of managing, publishing and/or providing searchable web access to information. Written by a knowledgeable web developer, this book demonstrates the simplicity, cost-effectiveness, and versatility of designing database driven web applications with Open Source resources. Case studies of real world implementations address both theoretical aspects and practical considerations of developing applications with the easy-to-use PHP scripting language and powerful MySQL relational database. Project organization and design issues are considered along with basic coding examples, accessibility standards and implementation advice.
Introduces popular Open Source database tools (MySQL/PHP) and basic development skills, bringing database driven technology within the reach of any web developerExplores strategies for improving content management, web publishing and information accessUses non-technical language and presents seven university library web database case studies"

The study and application of spatial information systems have been developed primarily from the use of computers in the geosciences. These systems have the principle functions of capturing, storing, representing, manipulating, and displaying data in 2-D and 3-D worlds. This book approaches its subject from the perspectives of informatics and geography, presenting methods of conceptual modeling developed in computer science that provide valuable aids for resolving spatial problems. This book is an essential textbook for both students and practitioners. It is indispensable for academic geographers, computer scientists, and the GIS professional.
Key Features
* Serves as the first comprehensive textbook on the field of Spatial Information Systems (also known as Geographic Information Systems)
* Contains extensive illustrations
* Presents numerous detailed examples

CompTIA (R) Data+ DA0-001 Exam Cram is an all-inclusive study guide designed to help you pass the CompTIA Data+ DA0-001 exam. Prepare for test day success with complete coverage of exam objectives and topics, plus hundreds of realistic practice questions. Extensive prep tools include quizzes, Exam Alerts, and our essential last-minute review CramSheet. The powerful Pearson Test Prep practice software provides real-time assessment and feedback with two complete exams. Covers the critical information needed to score higher on your Data+ DA0-001 exam! Understand data concepts, environments, mining, analysis, visualization, governance, quality, and controls Work with databases, data warehouses, database schemas, dimensions, data types, structures, and file formats Acquire data and understand how it can be monetized Clean and profile data so it;s more accurate, consistent, and useful Review essential techniques for manipulating and querying data Explore essential tools and techniques of modern data analytics Understand both descriptive and inferential statistical methods Get started with data visualization, reporting, and dashboards Leverage charts, graphs, and reports for data-driven decision-making Learn important data governance concepts

Mathematical Methods in Data Science introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. The mathematics is accompanied with examples and problems arising in data science to demonstrate advanced mathematics, in particular, data-driven differential equations used. Chapters also cover network analysis, ordinary and partial differential equations based on recent published and unpublished results. Finally, the book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. There are a number of books on mathematical methods in data science. Currently, all these related books primarily focus on linear algebra, optimization and statistical methods. However, network analysis, ordinary and partial differential equation models play an increasingly important role in data science. With the availability of unprecedented amount of clinical, epidemiological and social COVID-19 data, data-driven differential equation models have become more useful for infection prediction and analysis.

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.

Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment-and new types of transportation and users-based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management-detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19-and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality.

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!

In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.

Build a Large Language Model (from Scratch) teaches you how to:

• Plan and code all the parts of an LLM
• Prepare a dataset suitable for LLM training
• Fine-tune LLMs for text classification and with your own data
• Use human feedback to ensure your LLM follows instructions
• Load pretrained weights into an LLM

Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.

Blockchain has potential to revolutionize how manufacturers design, engineer, make and scale their products. Blockchain is gradually proving to be an effective "middleware" solution for enabling seamless interoperability within complex supply chains. Due to its technological nature, blockchain enables secure, transparent and fast data exchanges as well as allowing for the creation of immutable records databases The main advantage of Blockchain in Manufacturing Industries is product traceability, supply chain transparency, compliance monitoring, and auditability. Moreover, leveraging blockchain technology into a manufacturing enterprise can enhance its security and reduce the rates of systematic failures. So, blockchain is now used in various sectors of the manufacturing industry, such as automotive, aerospace, defense, pharmaceutical, consumer electronics, textile, food and beverages, and more. Hence, Blockchain should be seen as an investment in future-readiness and customer-centricity, not as an experimental technology - because, the evidence is overwhelming. This book will explore the strengths of Blockchain adaptation in Manufacturing Industries and Logistics Management, cover different use cases of Blockchain Technology for Manufacturing Industries and Logistics Management, and will discuss the role, impact and challenges of adopting Blockchain in Manufacturing industries and Logistics Management. The chapters will also provide the current open issues and future research trends of Blockchain, especially for Manufacturing Industries and Logistics, and will encapsulate quantitative and qualitative research for a wide spectrum of readers of the book.

With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data. Supported by query languages, databases, algorithms, platforms, analytics methods and machine and deep learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships. Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph data science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data. This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, artificial intelligence/machine and deep learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.

There has been a multitude of studies focused on the COVID-19 pandemic across fields and disciplines as all sectors of life have had to adjust the way things are done and adapt to the constantly shifting environment. These studies are crucial as they provide support and perspectives on how things are changing and what needs to be done to stay afloat. Connecting COVID-19-related studies and big data analytics is crucial for the advancement of industrial applications and research areas. Applied Big Data Analytics and Its Role in COVID-19 Research introduces the most recent industrial applications and research topics on COVID-19 with big data analytics. Featuring coverage on a broad range of big data technologies such as data gathering, artificial intelligence, smart diagnostics, and mining mobility, this publication provides concrete examples and cases of usage of data-driven projects in COVID-19 research. This reference work is a vital resource for data scientists, technical managers, researchers, scholars, practitioners, academicians, instructors, and students.