Continuing its commitment to developing and delivering industry-leading storage technologies, IBM® introduces the IBM Storwize® V7000 solution, an innovative new storage offering that delivers essential storage efficiency technologies and exceptional ease of use and performance, all integrated into a compact, modular design that is offered at a competitive, midrange price. The IBM Storwize V7000 solution incorporates some of the top IBM technologies typically found only in enterprise-class storage systems, raising the standard for storage efficiency in midrange disk systems. This cutting-edge storage system extends the comprehensive storage portfolio from IBM and can help change the way organizations address the ongoing information explosion. This IBM Redbooks® publication introduces the features and functions of the IBM Storwize V7000 system through several examples. This book is aimed at pre- and post-sales technical support and marketing, storage administrators, and will help you understand the architecture of the Storwize V7000, how to implement it, and take advantage of the industry leading functions and features.

Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.

Discover how to effectively administer large Apache Hadoop clusters with the Cloudera Administration Handbook. This guide offers step-by-step instructions and practical examples, enabling you to confidently set up and manage Hadoop environments using Cloudera Manager and CDH5 tools. Through this book, administrators or aspiring experts can unlock the power of distributed computing and streamline cluster operations. What this Book will help me do Gain in-depth understanding of Apache Hadoop architecture and its operational framework. Master the setup, configuration, and management of Hadoop clusters using Cloudera tools. Implement robust security measures in your cluster including Kerberos authentication. Optimize for reliability with advanced HDFS features like High Availability and Federation. Streamline cluster management and address troubleshooting effectively using best practices. Author(s) None Menon is an experienced technologist specializing in distributed computing and data infrastructure. With a strong background in big data platforms and certifications in Hadoop administration, None has helped enterprises optimize their cluster deployments. Their instructional approach combines clarity, practical insights, and a hands-on focus. Who is it for? This book is ideal for systems administrators, data engineers, and IT professionals keen on mastering Hadoop environments. It serves both beginners getting started with cluster setup and seasoned administrators seeking advanced configurations. If you're aiming to efficiently manage Hadoop clusters using Cloudera solutions, this guide provides the knowledge and tools you need.

"PostgreSQL 9 High Availability Cookbook" is a guide for PostgreSQL DBAs and developers looking to build a robust and highly available database ecosystem. Through over 100 tested recipes, it delves into vital topics like replication, clustering, and monitoring to ensure system reliability and uptime. What this Book will help me do Set up PostgreSQL replication to enhance data availability and reliability. Implement monitoring solutions to keep your database's performance and health under check. Learn to troubleshoot common database issues to reduce downtime. Configure connection pooling to optimize resource usage and ensure better scalability. Master techniques for clustering and partitioning large datasets to handle growing system needs. Author(s) The author, Shaun Thomas, is a seasoned PostgreSQL administrator with extensive experience in database tuning, high availability solutions, and Linux system management. Shaun brings practical insights from his years of professional practice, aiming to make complex topics approachable. Who is it for? This book caters to intermediate to advanced PostgreSQL administrators and developers. If you are seeking to enhance your database's performance, reliability, and resilience, this book is for you. With its practical recipe approach, it's a great fit for those who enjoy hands-on learning. Whether you're maintaining production systems or scaling for growth, this guide is your ally.

Boundary Element Method for Plate Analysis offers one of the first systematic and detailed treatments of the application of BEM to plate analysis and design. Aiming to fill in the knowledge gaps left by contributed volumes on the topic and increase the accessibility of the extensive journal literature covering BEM applied to plates, author John T. Katsikadelis draws heavily on his pioneering work in the field to provide a complete introduction to theory and application. Beginning with a chapter of preliminary mathematical background to make the book a self-contained resource, Katsikadelis moves on to cover the application of BEM to basic thin plate problems and more advanced problems. Each chapter contains several examples described in detail and closes with problems to solve. Presenting the BEM as an efficient computational method for practical plate analysis and design, Boundary Element Method for Plate Analysis is a valuable reference for researchers, students and engineers working with BEM and plate challenges within mechanical, civil, aerospace and marine engineering. One of the first resources dedicated to boundary element analysis of plates, offering a systematic and accessible introductory to theory and application Authored by a leading figure in the field whose pioneering work has led to the development of BEM as an efficient computational method for practical plate analysis and design Includes mathematical background, examples and problems in one self-contained resource

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

This IBM® Redbooks® publication focuses on gathering the correct technical information, and laying out simple guidance for optimizing code performance on IBM POWER8™ systems that run the AIX®, IBM i, or Linux operating systems. There is much straightforward performance optimization that can be performed with a minimum of effort and without extensive previous experience or in-depth knowledge. The POWER8 processor contains many new and important performance features, such as support for eight hardware threads in each core and support for transactional memory. POWER8 is a strict superset of IBM POWER7+™, and so all of the performance features of POWER7+, such as multiple page sizes, also appear in POWER8. Much of the technical information and guidance for optimizing performance on POWER8 presented in this guide also applies to POWER7+ and earlier processors, except where the guide explicitly indicates that a feature is new in POWER8. This guide strives to focus on optimizations that tend to be positive across a broad set of IBM POWER® processor chips and systems. Specific guidance is given for the POWER8 processor; however, the general guidance is applicable to the IBM POWER7+, IBM POWER7®, IBM POWER6®, IBM POWER5, and even to earlier processors. This guide is directed to personnel who are responsible for performing migration and implementation activities on IBM POWER8-based servers. This includes system administrators, system architects, network administrators, information architects, and database administrators (DBAs).

Get Started Scaling Your Database Infrastructure for High-Volume Big Data Applications “Understanding Big Data Scalability presents the fundamentals of scaling databases from a single node to large clusters. It provides a practical explanation of what ‘Big Data’ systems are, and fundamental issues to consider when optimizing for performance and scalability. Cory draws on many years of experience to explain issues involved in working with data sets that can no longer be handled with single, monolithic relational databases.... His approach is particularly relevant now that relational data models are making a comeback via SQL interfaces to popular NoSQL databases and Hadoop distributions.... This book should be especially useful to database practitioners new to scaling databases beyond traditional single node deployments.” —Brian O’Krafka, software architect presents a solid foundation for scaling Big Data infrastructure and helps you address each crucial factor associated with optimizing performance in scalable and dynamic Big Data clusters. Understanding Big Data Scalability Database expert Cory Isaacson offers practical, actionable insights for every technical professional who must scale a database tier for high-volume applications. Focusing on today’s most common Big Data applications, he introduces proven ways to manage unprecedented data growth from widely diverse sources and to deliver real-time processing at levels that were inconceivable until recently. Isaacson explains why databases slow down, reviews each major technique for scaling database applications, and identifies the key rules of database scalability that every architect should follow. You’ll find insights and techniques proven with all types of database engines and environments, including SQL, NoSQL, and Hadoop. Two start-to-finish case studies walk you through planning and implementation, offering specific lessons for formulating your own scalability strategy. Coverage includes Understanding the true causes of database performance degradation in today’s Big Data environments Scaling smoothly to petabyte-class databases and beyond Defining database clusters for maximum scalability and performance Integrating NoSQL or columnar databases that aren’t “drop-in” replacements for RDBMSes Scaling application components: solutions and options for each tier Recognizing when to scale your data tier—a decision with enormous consequences for your application environment Why data relationships may be even more important in non-relational databases Why virtually every database scalability implementation still relies on sharding, and how to choose the best approach How to set clear objectives for architecting high-performance Big Data implementations The Big Data Scalability Series is a comprehensive, four-part series, containing information on many facets of database performance and scalability. is the first book in the series. Understanding Big Data Scalability Learn more and join the conversation about Big Data scalability at bigdatascalability.com.

Capable of acquiring large volumes of data through sensors deployed in air, land, and sea, and making this information readily available in a continuous time frame, the science of geographical information system (GIS) is rapidly evolving. This popular information system is emerging as a platform for scientific visualization, simulation, and computation of spatio-temporal data. New computing techniques are being researched and implemented to match the increasing capability of modern-day computing platforms and easy availability of spatio-temporal data. This has led to the need for the design, analysis, development, and optimization of new algorithms for extracting spatio-temporal patterns from a large volume of spatial data. considers the computational aspects, and helps students understand the mathematical principles of GIS. It provides a deeper understanding of the algorithms and mathematical methods inherent in the process of designing and developing GIS functions. It examines the associated scientific computations along with the applications of computational geometry, differential geometry, and affine geometry in processing spatial data. It also covers the mathematical aspects of geodesy, cartography, map projection, spatial interpolation, spatial statistics, and coordinate transformation. The book discusses the principles of bathymetry and generation of electronic navigation charts. Computing in Geographic Information Systems The book consists of 12 chapters. Chapters one through four delve into the modeling and preprocessing of spatial data and prepares the spatial data as input to the GIS system. Chapters five through eight describe the various techniques of computing the spatial data using different geometric and statically techniques. Chapters nine through eleven define the technique for image registration computation and measurements of spatial objects and phenomenon. Examines cartographic modeling and map projection Covers the mathematical aspects of different map projections Explores some of the spatial analysis techniques and applications of GIS Introduces the bathymetric principles and systems generated using bathymetric charts Explains concepts of differential geometry, affine geometry, and computational geometry Discusses popular analysis and measurement methods used in GIS This text outlines the key concepts encompassing GIS and spatio-temporal information, and is intended for students, researchers, and professionals engaged in analysis, visualization, and estimation of spatio-temporal events.

Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals. Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, Basic Data Analysis for Time Series with R also features: Real-world examples to provide readers with practical hands-on experience Multiple R software subroutines employed with graphical displays Numerous exercise sets intended to support readers understanding of the core concepts Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will "learn data mining by doing data mining". By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book

PROVE THE VALUE OF YOUR HR PROGRAM WITH HARD DATA While corporate leaders may well know the value of human capital, they don’t always understand the extent to which the HR function contributes to the bottom line. So when times get tough and business budgets get cut, HR departments often take the first hit. In this groundbreaking guide, the cofounders of ROI Institute, Jack Phillips and Patti Phillips, provide the tools and techniques you need to use analytics to show top decision makers the value of HR in your organization. Focusing on three types of analytics--descriptive, predictive, and prescriptive-- Making Human Capital Analytics Work shows how you can apply analytics by: Developing relationships between variables Predicting the success of HR programs Determining the cost of intangibles that are otherwise diffi cult to value Showing the business value of particular HR programs Calculating and forecasting the ROI of various HR projects and programs Much more than a guide to using data collection and analysis, Making Human Capital Analytics Work is a template for spearheading large-scale change in your organization by dramatically influencing your department's overall image within the organization. The authors take you step-by-step through the processes of using hard data to drive decisions and demonstrate the tangible value of HR. You know that your department is more than administrative and transactional--that it's an integral player in your company's strategy. Apply the lessons in Making Human Capital Analytics Work and ensure that all other stakeholders know too.

Your hands-on guide to Azure SQL Database fundamentals Expand your expertise—and teach yourself the fundamentals of Microsoft Azure SQL Database. If you have previous programming experience but are new to Azure, this tutorial delivers the step-by-step guidance and coding exercises you need to master core topics and techniques. Discover how to: Perform Azure setup and configuration Explore design and security considerations Use programming and reporting services Migrate data Backup and sync data Work with scalability and high performance Understand the differences between SQL Server and Microsoft Azure SQL Database

The IBM® Distributed Virtual Switch 5000V (DVS 5000V) is a software-based network switching solution that is designed for use with the virtualized network resources in a VMware enhanced data center. It works with VMware vSphere and ESXi 5.0 and beyond to provide an IBM Networking OS management plane and advanced Layer 2 features in the control and data planes. It provides a large-scale, secure, and dynamic integrated virtual and physical environment for efficient virtual machine (VM) networking that is aware of server virtualization events, such as VMotion and Distributed Resource Scheduler (DRS). The DVS 5000V interoperates with any 802.1Qbg compliant physical switch to enable switching of local VM traffic in the hypervisor or in the upstream physical switch. Network administrators who are familiar with IBM System Networking switches can manage the DVS 5000V just like IBM physical switches by using advanced networking, troubleshooting, and management features to make the virtual switch more visible and easier to manage. This IBM Redbooks® publication helps the network and system administrator install, tailor, and quickly configure the IBM Distributed Virtual Switch 5000V (DVS 5000V) for a new or existing virtualization computing environment. It provides several practical applications of the numerous features of the DVS 5000V, including a step-by-step guide to deploying, configuring, maintaining, and troubleshooting the device. Administrators who are already familiar with the CLI interface of IBM System Networking switches will be comfortable with the DVS 5000V. Regardless of whether the reader has previous experience with IBM System Networking, this publication is designed to help you get the DVS 5000V functional quickly, and provide a conceptual explanation of how the DVS 5000V works in tandem with VMware.

Find guidance on using SAS for multiple imputation and solving common missing data issues.

Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data.

Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results.

Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures.

Discover the theoretical background and see extensive applications of the multiple imputation process in action.

This book is part of the SAS Press program.

Understand the concepts and techniques of analysis while learning to reason statistically.

Being an effective analyst requires that you know how to properly define a problem and apply suitable statistical techniques, as well as clearly and honestly communicate the results with information-rich visualizations and precise language. Being a well-informed consumer of analyses requires the same set of skills so that you can recognize credible, actionable research when you see it.

Robert Carver's Practical Data Analysis with JMP, Second Edition uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. It enables you to discriminate among and to use fundamental techniques of analysis, enabling you to engage in statistical thinking by analyzing real-world problems. “Application Scenarios” at the end of each chapter challenge you to put your knowledge and skills to use with data sets that go beyond mere repetition of chapter examples, and three new review chapters help readers integrate ideas and techniques. In addition, the scope and sequence of the chapters have been updated with more coverage of data management and analysis of data.

The book can stand on its own as a learning resource for professionals or be used to supplement a standard college-level introduction-to-statistics textbook. It includes varied examples and problems that rely on real sets of data, typically starting with an important or interesting research question that an investigator has pursued. Reflective of the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, economics, among

Practical Data Analysis with JMP, Second Edition introduces you to the major platforms and essential features of JMP and will leave you with a sufficient background and the confidence to continue your exploration independently.

This book is part of the SAS Press program.

Improve efficiency while reducing costs in clinical trials with centralized monitoring techniques using JMP and SAS.

International guidelines recommend that clinical trial data should be actively reviewed or monitored; the well-being of trial participants and the validity and integrity of the final analysis results are at stake. Traditional interpretation of this guidance for pharmaceutical trials has led to extensive on-site monitoring, including 100% source data verification. On-site review is time consuming, expensive (estimated at up to a third of the cost of a clinical trial), prone to error, and limited in its ability to provide insight for data trends across time, patients, and clinical sites. In contrast, risk-based monitoring (RBM) makes use of central computerized review of clinical trial data and site metrics to determine if and when clinical sites should receive more extensive quality review or intervention.

Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS presents a practical implementation of methodologies within JMP Clinical for the centralized monitoring of clinical trials. Focused on intermediate users, this book describes analyses for RBM that incorporate and extend the recommendations of TransCelerate Biopharm Inc., methods to detect potential patient-or investigator misconduct, snapshot comparisons to more easily identify new or modified data, and other novel visual and analytical techniques to enhance safety and quality reviews. Further discussion highlights recent regulatory guidance documents on risk-based approaches, addresses the requirements for CDISC data, and describes methods to supplement analyses with data captured external to the study database.

Given the interactive, dynamic, and graphical nature of JMP Clinical, any individual from the clinical trial team - including clinicians, statisticians, data managers, programmers, regulatory associates, and monitors - can make use of this book and the numerous examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data.

The analytical methods described in Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS enable the clinical trial team to take a proactive approach to data quality and safety to streamline clinical development activities and address shortcomings while the study is ongoing.

This book is part of the SAS Press

Key decisions determine the success of big data strategy Dynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance. Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include: Applying the elements of Dynamic Customer Strategy Acquiring, mining, and analyzing data Metrics and models for big data utilization Shifting perspective from model to customer Big data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.

" Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you're a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data: Statistical Analysis for Professional Success provides the answers to these questions and more. It will show you how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences. Author Peter Kenny-with deep experience in industry-believes that "while the methods of statistics can be complicated, the meaning of statistics is not." He first outlines the ways in which we are frequently misled by statistical results, either because of our lack of understanding or because we are being misled intentionally. Then he offers sound approaches for understanding and assessing statistical data to make excellent decisions. Kenny assumes no prior knowledge of statistical techniques; he explains concepts simply and shows how the tools are used in various business situations. With the arrival of Big Data, statistical processing has taken on a new level of importance. Kenny lays a foundation for understanding the importance and value of Big Data, and then he shows how mined data can help you see your business in a new light and uncover opportunity. Among other things, this book covers: How statistics can help you assess the probability of a successful outcome How data is collected, sampled, and best interpreted How to make effective forecasts based on the data at hand How to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposals How to commission a statistical analysis Arranged in seven parts-Uncertainties, Data, Samples, Comparisons, Relationships, Forecasts, and Big Data-" Better Business Decisions from Data is a guide for busy people in general management, finance, marketing, operations, and other business disciplines who run across statistics on a daily or weekly basis. You'll return to it again and again as new challenges emerge, making better decisions each time that boost your organization's fortunes—as well as your own.

" R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter. Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. "