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The transformation requires access to a reference data source that contains values used to clean and extend the input data. The reference data source must be a table in a SQL Server 2005 database. The match between the value in an input column and the value in the reference table can be an exact match or a fuzzy match. However, the transformation requires at least one column match to be configured for fuzzy matching. You can customize this transformation by specifying the maximum amount of memory, the row comparison algorithm, and the caching of indexes and reference tables that the transformation uses. The Fuzzy Lookup transformation includes three features for customizing the lookup it performs: maximum number of matches to return per input row, token delimiters, and similarity thresholds. The transformation provides a default set of delimiters used to tokenize the data, but you can add token delimiters to suit the needs of your data. The Delimiters property contains the default delimiters. Tokenization is important because it defines the units within the data that are compared to each other. The similarity thresholds can be set at the component and join levels. The join-level similarity threshold is available only when the transformation performs a fuzzy match between columns in the input and the reference table. You specify the similarity threshold by setting the MinSimilarity property at the component and join levels. Each match includes a similarity score and a confidence score. The similarity score is represented by a value between 0 and 1, where 1 means an exact match between the value in the input column and the value in the reference table. The confidence score, also a value between 0 and 1, indicates the confidence in the match. If no usable match is found, similarity and confidence scores of 0 are assigned to the row, and the output columns copied from the reference table will contain null values.
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Lesson Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
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Up to now in this chapter, you have used only relational sources for data mining. But you can also use a UDM cube as a data mining source. Using UDM cubes as sources for data mining is quite simple. When you mine OLAP cubes, your case table is a dimension. Any measure from any measure group that is, any fact table connected with the selected dimension can be used as a case-level column. Additionally, you can add other dimensions connected to the same measure group as nested tables. OLAP cubes are often appropriate sources for data mining. In an OLAP cube, you already have data that has been collected over time, merged, and cleansed. However, adding derived variables and additionally modifying the data is much easier in a relational source.
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Import-CSV C:\IdentifyUsers.csv | ForEach-Object Process {Disable-Mailbox Identity $_.Identity} Import-CSV C:\IdentifyUsers.csv | ForEach-Object Process {Remove-Mailbox Identity $_.Identity}
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//Loop through the reader and get our XML
Replication and Directory Partitions
This chapter will also look at some tools in Windows XP Professional that help make your deployment of Windows XP Professional easier. These tools include the File and Transfer Wizard, the User State Migration Tool (USMT), and Windows Installer. Lessons in this :
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Exercise 1: Taking Ownership of a File
2
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In the existing query window, type, highlight, and execute the following SQL statement:
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Resolve space issues. Update statistics. Evaluate index usage. Defragment and rebuild indexes. Create missing indexes and drop inappropriate indexes. Audit and analyze poorly written queries. Monitor transaction log size and database growth. Investigate locking and resolve deadlocks. Determine optimum RAID configuration.
Analyzing Queries
Centralized Storage of Configuration Data
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