Базы данных

If you’re in charge of database administration, developing database software, or looking for database solutions for your company, Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies can help you get a handle on this extremely popular relational database management system. Here you’ll find what’s new in the latest version; how to choose and install the right variation for your needs; how to monitor, maintain, and protect your data; and what it takes to keep your database healthy. You’ll discover how to: Build and maintain tables Design a database and communicate with it Retrieve, analyze, and report data Build solid, robust database applications Use the SQL Server Optimizer and Query Designer Navigate SQL Server with Visual Studio Develop useful reports with the Report Builder and Report Designer Create Business Intelligence solutions with Business Intelligence Development Studio Configure your server and perform major administrative tasks To help you quickly find what you need, Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies is divided into nine minibooks: Essential Concepts Designing and Using Databases Interacting With Your Data Database Programming Reporting Services Analysis Services Performance Tips and Tricks Database Administration Appendixes Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies gets you started, helps you solve problems, and will even answer your questions down the road!

If you've not programmed with Transact-SQL, this book is for you.It begins with an overview of SQL Server query operations and tools used with T-SQL, and covers both the 2005 and 2008 releases of SQL Server query tools and the query editor. The book then moves to show you how to design and build applications of increasing complexity. Other important tasks covered include full text indexing, optimizing query performance, and application design and security considerations. The companion website also provides all of the code examples from the book.

This book is written for SQL Server 2008. However, it does maintain roots going back a few versions and looks out for backward compatibility issues with SQL Server 2005 and SQL Server 2000. These versions are old enough that there is little to no time spent on them except in passing. The book is oriented around developing on SQL server. Most of the concepts are agnostic to what client language you use although the examples that leverage a client language general do so in C#. For those who are migrating from early versions of SQL Server, some “gotchas” that exist any time a product has versions are discussed to the extent that they seem to be a genuinely relevant issue. This book assumes that you have some experience with SQL Server and are at an intermediate to advanced level. The orientation of the book is highly developer focused. While there is a quick reference-oriented appendix, there is very little coverage given to beginner level topics. It is assumed that you already have experience with data manipulation language (DML) statements and know the basics of the mainstream SQL Server objects (views, stored procedures, user defined functions, etc.). If you would like to brush up on your knowledge before diving into this book, the author recommends reading Beginning SQL Server 2008 Programming first. There is very little overlap between the Beginning and Professional books and they are designed to work as a pair.

This friendly, easy-to-use guide shows experienced Access users how to use VBA (Visual Basic for Applications) to build Access databases and applications, but also covers programming fundamentals for nonprogrammers Includes practical, ready-to-use VBA code examples that readers can type or copy and paste from the Web into their own database projects Explains basic VBA skills and concepts for nonprogrammers, such as procedures, variables, and loops Covers more advanced topics, such as record sets and other programming activities that are unique to Access programming Author has written more than ninety computer books and has been working with databases since the early 1980s

If you run a small business, you know how important customer relationship management, or CRM, can be to your bottom line. And you know it can be a bit daunting. Well, not with GoldMine and GoldMine 8 For Dummies! GoldMine is business software designed to help you organize your contacts, track activities, schedule appointments, create reports that tell you how you’re doing, and make accurate business projections. GoldMine 8 is completely revised to make it more useful than ever, once you get the hang of it. That’s where GoldMine For Dummies comes to the rescue! Written by an authorized GoldMine trainer, this guide shows you how to: Set up GoldMine 8 and customize it for your business Create, view, and update customer records Schedule activities and manage leads Monitor your marketing efforts and see what’s working Predict sales based on past results Work with GoldMine’s e-mail system Organize and distribute information Access GoldMine from outside your office Integrate GoldMine with your company Web site Even if you’ve used a previous version of GoldMine, you’ll find some big changes in this newest revision, but GoldMine 8 For Dummies makes it easy to get up to speed. When you’ve read this book and discovered how GoldMine can help you keep track of your clients, reduce costs, and improve efficiency, you’ll think you’ve hit the motherlode!

Refocused and completely rewritten with the needs of business users and FileMaker Pro developers in mind, FileMaker Pro 9 Bible shows you how to design an industrial-strength database, make it intuitive, and automate it with scripts and programming. From the fundamentals to data modeling to writing no-fail code, you'll find the clear, step-by-step procedures, solid techniques, and detailed explanations you need to master this powerful software and build the right solutions for your needs.

With special focus on the next major release of MySQL, this resource provides a solid framework for anyone new to MySQL or transitioning from another database platform, as well as experience MySQL administrators. The high-profile author duo provides essential coverage of the fundamentals of MySQL database management—including MySQL’s unique approach to basic database features and functions—as well as coverage of SQL queries, data and index types, stores procedure and functions, triggers and views, and transactions. They also present comprehensive coverage of such topics as MySQL server tuning, managing storage engines, caching, backup and recovery, managing users, index tuning, database and performance monitoring, security, and more.

The heart of the book provides the complete set of models that will support most of an organization's core business functions, including universal meta models for enterprise-wide systems, business meta data and data stewardship, portfolio management, business rules, and XML, messaging, and transactions Developers can directly adapt these models to their own businesses, saving countless hours of development time Building effective meta data repositories is complicated and time-consuming, and few IT departments have the necessary expertise to do it right-which is why this book is sure to find a ready audience Begins with a quick overview of the Meta Data Repository Environment and the business uses of meta data, then goes on to describe the technical architecture followed by the detailed models

SQL Server 2008 is how-to guide for experienced DBAs. Tutorial-based, this book will get you over the learning curve of how to configure and administer SQL Server 2008. Whether you're an administrator or developer using SQL Server, you can't avoid wearing a DBA hat at some point. The book is loaded with unique tips and workarounds for the most difficult SQL Server admin issues, including managing and monitoring SQL Server, automating administration, security, performance tuning, scaling and replications, clustering, and backup and recovery. A companion website is also available.

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. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

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 for university instructors who adopt the book

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 usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

In an increasingly digital economy, mastering the quality of data is an increasingly vital yet still, in most organizations, a considerable task. The necessity of better governance and reinforcement of international rules and regulatory or oversight structures (Sarbanes Oxley, Basel II, Solvency II, IAS-IFRS, etc.) imposes on enterprises the need for greater transparency and better traceability of their data. All the stakeholders in a company have a role to play and great benefit to derive from the overall goals here, but will invariably turn towards their IT department in search of the answers. However, the majority of IT systems that have been developed within businesses are overly complex, badly adapted, and in many cases obsolete; these systems have often become a source of data or process fragility for the business. It is in this context that the management of ‘reference and master data’ or Master Data Management (MDM) and semantic modeling can intervene in order to straighten out the management of data in a forward-looking and sustainable manner. This book shows how company executives and IT managers can take these new challenges, as well as the advantages of using reference and master data management, into account in answering questions such as: Which data governance functions are available? How can IT be better aligned with business regulations? What is the return on investment? How can we assess intangible IT assets and data? What are the principles of semantic modeling? What is the MDM technical architecture? In these ways they will be better able to deliver on their responsibilities to their organizations, and position them for growth and robust data management and integrity in the future.

This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

The topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. The chapters in this book report on research about the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues.

Cutting-edge content and guidance from a data warehousing expert—now expanded to reflect field trends Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery. This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information. Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Provides the fundamentals, technologies, and best practices in designing, constructing and managing mission critical, energy efficient data centers Organizations in need of high-speed connectivity and nonstop systems operations depend upon data centers for a range of deployment solutions. A data center is a facility used to house computer systems and associated components, such as telecommunications and storage systems. It generally includes multiple power sources, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and security devices. With contributions from an international list of experts, The Data Center Handbook instructs readers to: Prepare strategic plan that includes location plan, site selection, roadmap and capacity planning Design and build «green» data centers, with mission critical and energy-efficient infrastructure Apply best practices to reduce energy consumption and carbon emissions Apply IT technologies such as cloud and virtualization Manage data centers in order to sustain operations with minimum costs Prepare and practice disaster reovery and business continuity plan The book imparts essential knowledge needed to implement data center design and construction, apply IT technologies, and continually improve data center operations.