In this IBM Redbooks publication, we discuss and describe a multidimensional data warehousing infrastructure that can enable solutions for complex problems in an efficient and effective manner. The focus of this infrastructure is the InfoSphere Warehouse Cubing Services Feature. With this feature, DB2 becomes the data store for large volumes of data that you can use to perform multidimensional analysis, which enables viewing complex problems from multiple perspectives, which provides more information for management business decision making. This feature supports analytic tool interfaces from powerful data analysis tools, such as Cognos 8 BI, Microsoft Excel, and Alphablox. This is a significant capability that supports and enhances the analytics that clients use as they work to resolve problems with an ever growing scope, dimension, and complexity. Analyzing problems by performing more detailed queries on the data and viewing the results from multiple perspectives yields significantly more information and insight. Building multidimensional cubes based on underlying DB2 relational tables, without having to move or replicate the data, enables significantly more powerful data analysis with less work and leads to faster problem resolution with the capability for more informed management decision making. This capability is known as No Copy Analytics and is made possible with InfoSphere Warehouse Cubing Services.

Data mining has evolved from the ethereal domain of the highly-skilled mathematician to the expert data miner's workbench tool and ultimately to widely accessible business applications. For decades, industry and academia have been engaged in far-reaching research and development of data mining. At the same time, businesses have been leveraging this research, exploiting a handful of algorithms most useful in finding information to help resolve business problems. Recent trends have made these algorithms and systems, which are rooted in solid research, available to a wide range of business users in easy-to-use forms. Large numbers of business analysts, who may not be data mining experts, can now solve high-value business problems using data mining technology embedded in database-resident business applications. In this IBM Redbooks publication, we discuss the methodology and selected techniques of embedded data mining and show how sophisticated technologies can be used in today's business environment to create significant business value. All this is enabled by the IBM DB2 Warehouse (DB2W) data mining capabilities. Using DB2W, we show examples of using data mining capabilities for such analytic functions as data modeling, scoring, and visualization. In addition, there are scenarios and examples that help in understanding where, when, and how to use data mining. For techniques, technical details, and practical examples, this is the book you need.

Formerly known as DB2® Warehouse, InfoSphere™ Warehouse enables a unified, powerful data warehousing environment. It provides access to structured and unstructured data, as well as operational and transactional data. In this IBM® Redbooks® publication, we provide a brief overview of InfoSphere Warehouse, but the primary objective is to discuss and describe the capabilities of one particular component of the InfoSphere Warehouse, which is InfoSphere Warehouse Cubing Services. InfoSphere Warehouse Cubing Services is designed to provide a multidimensional view of data stored in relational databases, for significantly improved query and analysis capabilities. For this, there are particular schema designs that are typically used for these data warehouse and data mart databases, called dimensional, or cube, models. Optimization techniques are used to dramatically improve the performance of the OLAP queries, which are a core component of data warehousing and analytics. InfoSphere Warehouse Cubing Services works with business intelligence (BI) tools, and clients, such as Cognos® , Alphablox, and Microsoft® Excel® , through client interfaces, to accelerate OLAP queries from many data sources. We describe these interfaces and provide examples of how to use them to improve the performance of your OLAP queries.

In this IBM Redbook we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence. Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model - which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level. There is technical content for designing and maintaining such an environment, but also business content. For example, we use case studies to demonstrate how dimensional modeling can impact the business intelligence requirements for your business initiatives. In addition, we provide a detailed discussion on the query aspects of BI and data modeling. For example, we discuss query optimization and how you can determine performance of the data model prior to implementation. You need a solid base for your data warehousing infrastructure . . . . a solid data model.

In this IBM Redbook we describe and discuss DB2 Data Warehouse Edition (DWE) Version 9.1, a comprehensive platform offering with functionality to build a business intelligence infrastructure for analytics and Web-based applications, and best practices for deployment. DB2 DWE integrates core components for data warehouse construction and administration, data mining, OLAP, and InLine Analytics and reporting. It extends the DB2 data warehouse with design-side tooling and runtime infrastructure for OLAP, data mining, InLine Analytics, and intra-warehouse data movement and transformation, on a common platform based on DB2 and WebSphere. The platform pillars are based on the technology of DB2, Rational Data Architect (for physical data modeling only), the SQL Warehousing Tool, Intelligent Miner, DB2 Cube Views, and Alphablox. DWE includes an Eclipse-based design environment, DWE Design Studio, that integrates the DWE products (with the exception of Alphablox and Query Patroller) with a common framework and user interface. The new SQL Warehousing Tool enables visual design of intra-warehouse, table-to-table data flows and control flows using generated SQL. DB2 Alphablox is the tool for developing custom applications with embedded analytics-based visual components. DWE enables faster time-to-value for enterprise analytics, while limiting the number of vendors, tools, skill sets and licenses required.