Data mining

SQL in a Nutshell applies the eminently useful "e;Nutshell"e; format to Structured Query Language (SQL), the elegant--but complex--descriptive language that is used to create and manipulate large stores of data.

Do you enjoy writing software, except for the database code?

SQL (Structured Query Language) is a standard programming language for generating, manipulating, and retrieving information from a relational database.

However excellent they are, most computer books are inherently passive--readers simply take in text without having any opportunity to react to it.

Big Data: when the size and performance requirements for data management become significant design and decision factors for implementing a data management and analysis system.

Packed with practical, freely available backup and recovery solutions for Unix, Linux, Windows, and Mac OS X systems -- as well as various databases -- this new guide is a complete overhaul of Unix Backup & Recovery by the same author, now revised and expanded with over 75% new material.

Oracle is an enormous system, with myriad technologies, options, and releases.

The implementation of stored procedures in MySQL 5.

PL/SQL, Oracle's programming language for stored procedures, delivers a world of possibilities for your database programs.

In this compact book, Steven Feuerstein, widely recognized as one of the world's leading experts on the Oracle PL/SQL language, distills his many years of programming, teaching, and writing about PL/SQL into a set of best practices-recommendations for developing successful applications.

Not a reference book, and not a tutorial either, the new second edition of the highly regarded Access Cookbook is an uncommonly useful collection of solutions to problems that Access users and developers are likely to face as they attempt to build increasingly complex applications.

Oracle system performance inefficiencies often go undetected for months or even years--even under intense scrutiny--because traditional Oracle performance analysis methods and tools are fundamentally flawed.

The books in O'Reilly's Oracle series are authoritative -- they tell the whole story about complex topics, ranging from performance tuning to the use of packages in PL/SQL to new technologies like Power Objects.

This unique cookbook contains a wealth of solutions to problems that SQL programmers face all the time.

Whether you're running Access, MySQL, SQL Server, Oracle, or PostgreSQL, this book will help you push the limits of traditional SQL to squeeze data effectively from your database.

Although MySQL's source code is open in the sense of being publicly available, it's essentially closed to you if you don't understand it.

Radio Frequency Identification (RFID) is rapidly changing theway businesses track inventory and assets.

A poorly performing database application not only costs users time, but also has an impact on other applications running on the same computer or the same network.

The vast majority of Oracle SQL books discuss some syntax, provide the barest rudiments of using Oracle SQL, and perhaps include a few simple examples.

Sometimes the simplest answer is the best.

Despite its wide availability and usage, few developers and DBAs have mastered the true power of Oracle SQLPlus.

The Internet, with its profusion of information, has made us hungry for ever more, ever better data.

A comprehensive guide for IoT engineers, this book integrates data science, machine learning, and systems analytics to provide a robust understanding of modern techniques.

This book is dedicated to exploring and explaining time series event detection in databases.

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge.

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge.

Graphical models are of increasing importance in applied statistics, and in particular in data mining.

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis.

Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them.

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.

This is an applied handbook for the application of data mining techniques in the CRM framework.

Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data.

Understand how to use the new features of Microsoft SQL Server 2008 for data mining by using the tools in Data Mining with Microsoft SQL Server 2008, which will show you how to use the SQL Server Data Mining Toolset with Office 2007 to mine and analyze data.

A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data.

Provides readers with the methods, algorithms, and means to perform text mining tasks This book is devoted to the fundamentals of text mining using Perl, an open-source programming tool that is freely available via the Internet (www.

The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner.

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball.

Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball.

This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.

This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.

This book serves as a current resource for Photoplethysmogram (PPG) signal analysis using MATLAB(R).

This book serves as a current resource for Photoplethysmogram (PPG) signal analysis using MATLAB(R).

Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques.

Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques.

This book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs.

This book provides comprehensive details of all Swarm Intelligence based Techniques available till date in a comprehensive manner along with their mathematical proofs.

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples.

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples.

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things.

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things.