DESCRIPTION Users expect search to be simple: They enter a few terms and expect perfectly-organized, relevant results instantly. But behind this simple user experience, complex machinery is at work. Whether using Elasticsearch, Solr, or another search technology, the solution is never one size fits all. Returning the right search results requires conveying domain knowledge and business rules in the search engine's data structures, text analytics, and results ranking capabilities. Relevant Search demystifies relevance work. Using Elasticsearch, it tells how to return engaging search results to users, helping readers understand and leverage the internals of Lucene-based search engines. The book walks through several real-world problems using a cohesive philosophy that combines text analysis, query building, and score shaping to express business ranking rules to the search engine. It outlines how to guide the engineering process by monitoring search user behavior and shifting the enterprise to a search-first culture focused on humans, not computers. It also shows how the search engine provides a deeply pluggable platform for integrating search ranking with machine learning, ontologies, personalization, domain-specific expertise, and other enriching sources. KEY FEATURES Highly relevant, concrete, hands-on guide Digs deep into search engine technology Contains essential tools, tips, and strategies for building engaging search engines AUDIENCE For readers who can code moderately complex tasks. ABOUT THE TECHNOLOGY Lucene is the underlying technology that backs both Elasticsearch and Solr. Dominant search engines are based upon Lucene and since Lucene itself is based upon the strong foundation of Information Retrieval research, the book will be applicable to almost any search technology available now or in the foreseeable future.

As a society that relies on technology to thrive, we face a growing number of potentially catastrophic threats to network security daily. DATABASE SECURITY delivers the know-how and skills IT Professionals must have to protect technology infrastructures, intellectual property, and the company's prosperity. From database installation and testing to auditing and SQL Injection, DATABASE this text delves into the essential processes and protocols that prevent intrusions, while reinforcing each topic with eye-opening, real-world examples. Unlike most texts on database security, which take a computer scientist's analytical approach, Database Security focuses on implementation, and was written expressly for the expanding field of Information Technology careers.

Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward!

Pulling aside the curtain of 'Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals - how data is generated, organized, stored, evaluated, and maintained - has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as well as business analysts at all career stages and data analytics students.

The Official (ISC)2 (R) Guide to the CISSP (R)-ISSEP (R) CBK (R) provides an inclusive analysis of all of the topics covered on the newly created CISSP-ISSEP Common Body of Knowledge. The first fully comprehensive guide to the CISSP-ISSEP CBK, this book promotes understanding of the four ISSEP domains: Information Systems Security Engineering (ISSE); Certification and Accreditation; Technical Management; and an Introduction to United States Government Information Assurance Regulations. This volume explains ISSE by comparing it to a traditional Systems Engineering model, enabling you to see the correlation of how security fits into the design and development process for information systems. It also details key points of more than 50 U.S. government policies and procedures that need to be understood in order to understand the CBK and protect U.S. government information. About the Author Susan Hansche, CISSP-ISSEP is the training director for information assurance at Nortel PEC Solutions in Fairfax, Virginia. She has more than 15 years of experience in the field and since 1998 has served as the contractor program manager of the information assurance training program for the U.S. Department of State.

This book provides readers to the vision of Society 5.0, which was originally proposed in the fifth Basic Science and Technology Plan by Japan's government for a technology-based, human-centered society, emerging from the 4th industrial revolution (Industry 4.0). The implementation of AI and other modern techniques in a smart society requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. Features Provides an overview of basic concepts of Society 5.0 as well as the main pillars that supports the implementation of Society 5.0. Contains the most recent research analysis in the domain of computer vision, signal processing and computing sciences for facilitating the smart homes, buildings, transport, facilities, environmental conditions and cities, and the benefits these offer to a nation. Presents the readers with practical approaches of using AI and other algorithms for smart ecosystem to deals with human dynamics, the social objects, and their relations. Deals with the utilization of AI tools and other modern techniques for smart society as well as the current challenging issues and its solutions for transformation to Society 5.0. This book is aimed at graduate and post graduate students, researchers, academicians working in the field of computer science, artificial intelligence, and machine learning.

The crypto era has arrived, and business will never be the same. Real applications of crypto technology are growing exponentially: cryptocurrency payments are moving frictionlessly across borders; NFTs are generating real value for creators and consumers alike; and new blockchain-enabled business models are being built around decentralized finance and Web3. What do you and your company need to know and do today to create new opportunities and avoid disruption? Crypto: The Insights You Need from Harvard Business Review will show you how innovative organizations of all kinds are embracing decentralized technology, reinventing themselves, and thriving in the new age of crypto. Business is changing. Will you adapt or be left behind? Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues-blockchain, cybersecurity, AI, and more-each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow. You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas-and prepare you and your company for the future.

You may know SQL basics, but are you taking advantage of its expressive power? This second edition applies a highly practical approach to Structured Query Language (SQL) so you can create and manipulate large stores of data. Based on real-world examples, this updated cookbook provides a framework to help you construct solutions and executable examples in several flavors of SQL, including Oracle, DB2, SQL Server, MySQL, and PostgreSQL. SQL programmers, analysts, data scientists, database administrators, and even relatively casual SQL users will find SQL Cookbook to be a valuable problem-solving guide for everyday issues. No other resource offers recipes in this unique format to help you tackle nagging day-to-day conundrums with SQL. The second edition includes: Fully revised recipes that recognize the greater adoption of window functions in SQL implementations Additional recipes that reflect the widespread adoption of common table expressions (CTEs) for more readable, easier-to-implement solutions New recipes to make SQL more useful for people who aren't database experts, including data scientists Expanded solutions for working with numbers and strings Up-to-date SQL recipes throughout the book to guide you through the basics

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Assess data engineering problems using an end-to-end data framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle

The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way. At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.

Leverage the full power of Bayesian analysis for competitive advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use Bayesian analytics to solve a wide array of real problems. Carlberg clarifies terminology that often bewilders analysts, and offers sample R code to take advantage of the rethinking package in R and its gateway to Stan. As you incorporate these Bayesian approaches into your analytical toolbox, you'll build a powerful competitive advantage for your organization-and yourself. Explore key ideas and strategies that underlie Bayesian analysis Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function Manage text values as if they were numeric Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC) Use MCMC to optimize execution speed in high-complexity problems Discover when frequentist methods fail and Bayesian methods are essential-and when to use both in tandem

Market Basket Analysis (MBA) provides the ability to continually monitor the affinities of a business and can help an organization achieve a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants in each individual event. In the past however, the use of these powerful tools in tandem led to performance degradation and resulted in unactionable and even damaging information. Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance presents an innovative, soup-to-nuts approach that successfully combines what was previously incompatible, without degradation, and uses the relational architecture already in place. Built around two main chapters, Market Basket Solution Definition and Time Variant Solution Definition, it provides a tangible how-to design that can be used to facilitate MBA within the context of a data warehouse. Presents a solution for creating home-grown MBA data marts Includes database design solutions in the context of Oracle, DB2, SQL Server, and Teradata relational database management systems (RDBMS) Explains how to extract, transform, and load data used in MBA and Time Variant solutions The book uses standard RDBMS platforms, proven database structures, standard SQL and hardware, and software and practices already accepted and used in the data warehousing community to fill the gaps left by most conceptual discussions of MBA. It employs a form and language intended for a data warehousing audience to explain the practicality of how data is delivered, stored, and viewed. Offering a comprehensive explanation of the applications that provide, store, and use MBA data, Data Warehouse Designs provides you with the language and concepts needed to require and receive information that is relevant and actionable.

The end of the 20th century witnessed an information revolution that introduced a host of new economic efficiencies. This economic change was underpinned by rapidly growing networks of infrastructure that have become increasingly complex. In this new era of global security we are now forced to ask whether our private efficiencies have led to public vulnerabilities, and if so, how do we make ourselves secure without hampering the economy. In order to answer these questions, Sean Gorman provides a framework for how vulnerabilities are identified and cost-effectively mitigated, as well as how resiliency and continuity of infrastructures can be increased. Networks, Security and Complexity goes on to address specific concerns such as determining criticality and interdependency, the most effective means of allocating scarce resources for defense, and whether diversity is a viable strategy. The author provides the economic, policy, and physics background to the issues of infrastructure security, along with tools for taking first steps in tackling these security dilemmas. He includes case studies of infrastructure failures and vulnerabilities, an analysis of threats to US infrastructure, and a review of the economics and geography of agglomeration and efficiency. This critical and controversial book will garner much attention and spark an important dialogue. Policymakers, security professionals, infrastructure operators, academics, and readers following homeland security issues will find this volume of great interest.

What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https: //www.functionalaestheticsbook.com

This new volume explores a plethora of blockchain-based solutions for big data and IoT applications, looking at advances in real-world applications in several sectors, including higher education, cybersecurity, agriculture, business and management, healthcare and biomedical science, construction and project management, smart city development, and others. Chapters explore emerging technology to combat the ever-increasing threat of security to computer systems and offer new architectural solutions for problems encountered in data management and security. The chapters help to provide a high level of understanding of various blockchain algorithms along with the necessary tools and techniques. The novel architectural solutions in the deployment of blockchain presented here are the core of the book.

While most discoverability evaluation studies in the Library and Information Science field discuss the intersection of discovery layers and library systems, this book looks specifically at digital repositories, examining discoverability from the lenses of system structure, user searches, and external discovery avenues. Discoverability, the ease with which information can be found by a user, is the cornerstone of all successful digital information platforms. Yet, most digital repository practitioners and researchers lack a holistic and comprehensive understanding of how and where discoverability happens. This book brings together current understandings of user needs and behaviors and poses them alongside a deeper examination of digital repositories around the theme of discoverability. It examines discoverability in digital repositories from both user and system perspectives by exploring how users access content (including their search patterns and habits, need for digital content, effects of outreach, or integration with Wikipedia and other web-based tools) and how systems support or prevent discoverability through the structure or quality of metadata, system interfaces, exposure to search engines or lack thereof, and integration with library discovery tools. Discoverability in Digital Repositories will be particularly useful to digital repository managers, practitioners, and researchers, metadata librarians, systems librarians, and user studies, usability and user experience librarians. Additionally, and perhaps most prominently, this book is composed with the emerging practitioner in mind. Instructors and students in Library and Information Science and Information Management programs will benefit from this book that specifically addresses discoverability in digital repository systems and services.

Blockchain technologies, as an emerging distributed architecture and computing paradigm, have accelerated the development/application of the Cloud/GPU/Edge Computing, Artificial Intelligence, cyber physical systems, social networking, crowdsourcing and crowdsensing, 5G, trust management, and finance. The popularity and rapid development of Blockchain brings many technical and regulatory challenges for research and academic communities. This book will feature contributions from experts on topics related to performance, benchmarking, durability, robustness, as well data gathering and management, algorithms, analytics techniques for transactions processing, and implementation of applications.

Designed to offer an accessible set of case studies and analyses of ethical dilemmas in data science. This book will be suitable for technical readers in data science who want to understand diverse ethical approaches to AI.

'Natural Language Processing in the Real World' is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real-world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects. .

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Every day millions of people capture, store, transmit, and manipulate digital data. Unfortunately free access digital multimedia communication also provides virtually unprecedented opportunities to pirate copyrighted material. Providing the theoretical background needed to develop and implement advanced techniques and algorithms, Digital Watermarking and Steganography-

- Demonstrates how to develop and implement methods to guarantee the authenticity of digital media

- Explains the categorization of digital watermarking techniques based on characteristics as well as applications

- Presents cutting-edge techniques such as the GA-based breaking algorithm on the frequency-domain steganalytic system.

The popularity of digital media continues to soar. The theoretical foundation presented within this valuable reference will facilitate the creation on new techniques and algorithms to combat present and potential threats against information security.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

The internet has launched the world into an era into which enormous amounts of data are generated every day through technologies with both positive and negative consequences. This often refers to big data . This book explores big data in organisations operating in the criminology and criminal justice fields. Big data entails a major disruption in the ways we think about and do things, which certainly applies to most organisations including those operating in the criminology and criminal justice fields. Big data is currently disrupting processes in most organisations - how different organisations collaborate with one another, how organisations develop products or services, how organisations can identify, recruit, and evaluate talent, how organisations can make better decisions based on empirical evidence rather than intuition, and how organisations can quickly implement any transformation plan, to name a few. All these processes are important to tap into, but two underlying processes are critical to establish a foundation that will permit organisations to flourish and thrive in the era of big data - creating a culture more receptive to big data and implementing a systematic data analytics-driven process within the organisation. Written in a clear and direct style, this book will appeal to students and scholars in criminology, criminal justice, sociology, and cultural studies but also to government agencies, corporate and non-corporate organisations, or virtually any other institution impacted by big data.

Since databases are the primary repositories of information for today's organizations and governments, database security has become critically important. Introducing the concept of multilevel security in relational databases, this book provides a comparative study of the various models that support multilevel security policies in the relational database-illustrating the strengths and weaknesses of each model. Multilevel Security for Relational Databases covers multilevel database security concepts along with many other multilevel database security models and techniques. It presents a prototype that readers can implement as a tool for conducting performance evaluations to compare multilevel secure database models. The book supplies a complete view of an encryption-based multilevel security database model that integrates multilevel security for the relational database with a system that encrypts each record with an encryption key according to its security class level. This model will help you utilize an encryption system as a second security layer over the multilevel security layer for the database, reduce the multilevel database size, and improve the response time of data retrieval from the multilevel database. Considering instance-based multilevel database security, the book covers relational database access controls and examines concurrency control in multilevel database security systems. It includes database encryption algorithms, simulation programs, and Visual studio and Microsoft SQL Server code.