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Big data, data mining and machine learning : value creation for business leaders and practitioners
\"An expert guide to high performance computing architectures and how they relate to analytics and data miningWith the exponential growth of data comes an ever-increasing need to process and analyze so-called Big Data. High Performance Data Mining and Big Data Analytics provides a comprehensive view of the recent trend toward high performance computing architectures and its natural connection to analytics and data mining. You'll find coverage of topics including: big data, high performance computing for analytics, massively parallel processing (MPP) databases, in-memory analytics, implementation of machine learning algorithms for big data platforms, text analytics, analytics environments, the analytics lifecycle, general applications, as well as a variety of cases. Offers coverage of business analytics, predictive modeling, and fact-based management Includes case studies featuring multinational companies Explores recent trends in high performance computing architectures relating to data mining Filled with case studies, High Performance Data Mining and Big Data Analytics provides a thorough grounding for optimally putting data mining and big data analytics to work for your organization\"-- Provided by publisher.
Book
Advanced Database Systems
This book provides an in-depth study of the advanced concepts and technologies in database systems. It covers topics such as distributed databases, object-oriented databases, data mining, and big data analytics. The book is written for students and professionals in the field of computer science who want to enhance their knowledge and skills in advanced database technologies. This book will provide you with a solid understanding of the latest developments in database systems and their applications.
eBook
Survey of vector database management systems
There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely the ambiguity of semantic similarity, large size of vectors, high cost of similarity comparison, lack of structural properties that can be used for indexing, and difficulty of efficiently answering “hybrid” queries that jointly search both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning techniques based on randomization, learned partitioning, and “navigable” partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, distributed query processing, data manipulation queries, and hardware accelerated query execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including “native” systems that are specialized for vectors and “extended” systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally outline research challenges and point the direction for future work.
Journal Article
Business modeling and data mining
Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them.The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems.
eBook
Foundations for architecting data solutions : managing successful data projects
While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types. Use guidelines to evaluate and select data management solutions. Reduce risk related to technology, your team, and vague requirements. Explore system interface design using APIs, REST, and pub/sub systems. Choose the right distributed storage system for your big data system. Plan and implement metadata collections for your data architectureUse data pipelines to ensure data integrity from source to final storage. Evaluate the attributes of various engines for processing the data you collect.
Book
Redis 4.x cookbook
Redis is a popular key-value store database used commonly across many enterprises. Based on the latest version of Redis 4.x, this book provides useful recipes to help you overcome any obstacle when it comes to the different tasks associated with Redis - from working with data types to administering and troubleshooting your Redis solution.
eBook
Beginning Apache Pig : big data processing made easy
\"Learn to use Apache Pig to develop lightweight big data applications easily and quickly. This book shows you many optimization techniques and covers every context where Pig is used in big data analytics. Beginning Apache Pig shows you how Pig is easy to learn and requires relatively little time to develop big data applications. The book is divided into four parts: the complete features of Apache Pig; integration with other tools; how to solve complex business problems; and optimization of tools. You'll discover topics such as MapReduce and why it cannot meet every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be created; submitting Pig jobs using Hue; and working with Oozie. You'll also see how to extend the framework by writing UDFs and custom load, store, and filter functions. Finally you'll cover different optimization techniques such as gathering statistics about a Pig script, joining strategies, parallelism, and the role of data formats in good performance. What You Will Learn* Use all the features of Apache Pig* Integrate Apache Pig with other tools* Extend Apache Pig* Optimize Pig Latin code* Solve different use cases for Pig LatinWho This Book Is ForAll levels of IT professionals: architects, big data enthusiasts, engineers, developers, and big data administrators.\"-- Provided by publisher.
Book
SQL Injection Strategies
Learn to exploit vulnerable database applications using SQL injection tools and techniques, while understanding how to effectively prevent attacks Key Features * Understand SQL injection and its effects on websites and other systems * Get hands-on with SQL injection using both manual and automated tools * Explore practical tips for various attack and defense strategies relating to SQL injection Book Description SQL injection (SQLi) is probably the most infamous attack that can be unleashed against applications on the internet. SQL Injection Strategies is an end-to-end guide for beginners looking to learn how to perform SQL injection and test the security of web applications, websites, or databases, using both manual and automated techniques. The book serves as both a theoretical and practical guide to take you through the important aspects of SQL injection, both from an attack and a defense perspective. You'll start with a thorough introduction to SQL injection and its impact on websites and systems. Later, the book features steps to configure a virtual environment, so you can try SQL injection techniques safely on your own computer. These tests can be performed not only on web applications but also on web services and mobile applications that can be used for managing IoT environments. Tools such as sqlmap and others are then covered, helping you understand how to use them effectively to perform SQL injection attacks. By the end of this book, you will be well-versed with SQL injection, from both the attack and defense perspective. What you will learn * Focus on how to defend against SQL injection attacks * Understand web application security * Get up and running with a variety of SQL injection concepts * Become well-versed with different SQL injection scenarios * Discover SQL injection manual attack techniques * Delve into SQL injection automated techniques Who this book is for This book is ideal for penetration testers, ethical hackers, or anyone who wants to learn about SQL injection and the various attack and defense strategies against this web security vulnerability. No prior knowledge of SQL injection is needed to get started with this book.
eBook