The key to client/server computing. Transaction processing techniques are deeply ingrained in the fields of databases and operating systems and are used to monitor, control and update information in modern computer systems. This book will show you how large, distributed, heterogeneous computer systems can be made to work reliably. Using transactions as a unifying conceptual framework, the authors show how to build high-performance distributed systems and high-availability applications with finite budgets and risk. The authors provide detailed explanations of why various problems occur as well as practical, usable techniques for their solution. Throughout the book, examples and techniques are drawn from the most successful commercial and research systems. Extensive use of compilable C code fragments demonstrates the many transaction processing algorithms presented in the book. The book will be valuable to anyone interested in implementing distributed systems or client/server architectures.
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author--a noted expert on the topic--explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: Explores big data and cloud computing Examines deep learning Includes information on convolutional neural networks (CNN) Offers reinforcement learning Contains semi-supervised learning and S3VM Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
Provides an introduction to the theory and concepts underlying data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This title also provides a working knowledge of data integration concepts and techniques.
Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.
Offers a new approach to managing data Comprehensive coverage on building Big Data systems Works an example Big Data system to illustrate key concepts
Now fully updated, this bestselling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help users gain an intuitive understanding of the concepts and tools for building intelligent systems.t systems.