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1 input, 1 output The first question to answer is: why is Data Mining Query part of Data Flow and the Control Flow That s easy it depends on what the specified Data Mining Extensions (DMX) query is for. For complete flexibility the task is included as both a Control Flow task and as a Data Flow transformation component. In a nutshell, the data-mining implementation in SQL Server 2005 is all about the discovery of factually correct forecasted trends in data. This is configured within SSAS against one of the provided data-mining algorithms. The DMX query requests a predictive set of results from one or more such models built on the same mining structure. It can be a requirement to retrieve predictive information about the same data calculated using the different available algorithms. For further information, see entry on the Data Mining Query task in 6.

Atomic data types are indivisible data types that derive from the xs:anyAtomicType type. Examples include xs:boolean, xs:date, and xs:integer. List data types are types that are constructed of sequences of other types. Union data types are constructed from the ordered union of two or more data types, or a restricted subset of a data type. The XML

Schema 1.1 Part 2: Datatypes specification working draft (www.w3.org/TR/xmlschema11-2/ #ordinary-built-ins) defines no built-in union data types.

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1 input, 1 output The Fuzzy Grouping transformation is one of my personal favorites. Straight out of Microsoft Research Labs comes this crazy, intriguing, and initially unpredictable functionality . . . that gives me a headache when I think about how it must work and the fact that it can be used as a transformation in an enterprise-level ETL tool! In the broadest sense, Fuzzy Grouping is for use in cleansing data. By setting and tweaking task properties, you can achieve great results because the task interprets input data and makes intelligent decisions about its uniqueness. Upon finding a group of what it considers identical data, it selects a single canonical row to represent the group. These unique rows are routed out as task output. Figure 10-3 shows the configuration screen for this component.

An axis specifier indicates the relationship between the nodes selected by the location step and the context node. Examples of axis specifiers include child, parent, and ancestor.

Figure 10-3. A deceptively simple configuration screen for specifying columns Based upon a fuzzy-matching algorithm, all columns are processed and grouping is based on a given similarity index a calculated value between 0 and 1. (You can think of it as a score.) A similarity index of 1 means values are identical. When selecting columns to use from the Available Input Columns list, the Pass Through property indicates that the column should be copied into the output-columns collection directly and untouched. Table 10-4 shows the per-column properties available for configuration. Table 10-4. Fuzzy Match Operation Properties, per Column

At this stage, organizations should be taking the following actions: 1. Develop basic SSAS cubes to allow business users to explore the data running behind key dashboards in an easy-to-use, high-performance environment. 2. Think through a design approach to determine how to best deliver trend-based data via a data mart and SSAS cube. 3. Develop the data mart and SSAS cube based on the SSIS toolkit and analysis services tools bundled with SQL Server 2008. 4. Evaluate the limitations of the overall BI platform you ve established to determine if your next initiative should involve revisions to your existing SQL Server based solution or a migration to a more comprehensive solution based on PerformancePoint. In 6, we will address these steps in detail, but at this stage, we ll walk through the development of one sample cube and touch on the high-level concepts that must be addressed to deliver on the actions outlined in this section.

Bulk Copy Program (BCP)

Name of the selected column that is suspected of containing duplicate values. Name of the column once it gets added to the output stream. Name of the column that will contain the canonical value. A mixture of fuzzy and exact-match types can be used across the selected columns. An exact match is preferable for performance benefits and accuracy. Leveraging columns that can be compared directly can help the fuzzy matching by narrowing the potential results on which the fuzzy match is attempted.

This is a command-line utility supplied with SQL Server for the purpose of quickly loading large datasets into tables.

Only columns of data type DT_WSTR and DT_STR can be analyzed. Any data type except DT_TEXT, DT_NTEXT, and DT_IMAGE can be matched.

Business Intelligence Development Studio (BIDS)

Your first step is to develop an analysis services cube. That cube forms the basis for everything else that you do in this intermediate stage. Exercise 4-2 walks you through the creation of an initial cube for Dyno Technologies. Later, you ll see that cube extended to deliver tools that analysts can use to explore their sales and marketing data in an ad hoc fashion.

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