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Underscore (_). To use this character as a sentence boundary, neither the character to the left nor to the right of the underscore can be a letter. All Unicode characters that are less than or equal to 0x19, or greater than or equal to 0x7b. Combinations of numbers, punctuation marks, and alphabetical characters. These could mean, for example, A23B#99 returns the term A23B. The characters, %, @, &, $, #, *, :, ;, ., , , !, , <, >, +, =, ^, ~, |, \, /, (, ), [, ], {, }, , and . The fourth (and final) pass separates the data into individual words (or tokens) by looking for the following word boundaries: Space Tab ASCII 0x0d (carriage return) ASCII 0x0a (line feed) This leaves a number of tokens for each value that the Term Extraction engine can process and decide to retain as terms or be discarded. Of course, it is entirely possible to write a completely original term-extraction engine by writing a new custom component, even if it inherits parts of the original component to perform the basic processing.

In dealing with tree structures in T-SQL, we use an algorithm to deal with the tree breadthwise, rather than depthwise. For example, consider the following tree:

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In functional programming languages, it would have been common to recursively traverse a tree one node at a time, touching one node at a time. So you would go from node 1, to 1.1, to 1.1.1, back to 1.1, to 1.1.2, back to 1.1, back to 1, on to 1.2, etc. This works great for pointer-based languages, but in relational languages like T-SQL, a much better algorithm is to access all the children of a node in one query, then all the children of these nodes, and so on. So we would get node 1, then 1.1 and 1.2, and finally 1.1.1, 1.1.2, 1.2.1, and 1.2.2. It is a fantastic algorithm for dealing with sets, but in the following code sample, you will see that it was not very clean to implement in SQL Server 2000.

1 input, 1 output Just when you thought it was safe to go back into the water . . . here is another term-based transformation: Term Lookup. This task wraps the functionality of the Term Extraction transform and uses the values extracted to compare to a reference table, just like the Lookup transform. To get a better idea of how this all works, especially since it is such an integral part of the task, read the preceding section on the Term Extraction component. Figure 10-32 shows how you specify the column mapping for the lookup. The Term Lookup tab of the task editor allows the selection of pass-through columns and the mapping of columns from the available input columns to the available reference columns specifying how to perform the lookup. In terms of function, the transform extracts the terms and then performs a lookup against values in a specified reference table. A count is performed to see how many times a match occurs between the input term and the reference table, which gets written to the task output. A column is added to the output containing the extracted term, and another is added containing the number of occurrences. These new columns are called Term and Frequency. The reference table is cached in memory for performance reasons.

maximum possible value for an int, so we need to use longs instead for these variables; however, we can t cast directly from a SqlInt32 to a long, so we have to convert them to ints first: long timeticks = (int)ticksParmValue; int connections = (int)connParmValue; long cpuBusy = timeticks * (int)cpuParmValue; long idle = timeticks * (int)idleParmValue; long ioBusy = timeticks * (int)ioParmValue; int packErrors = (int)packErrsParmValue; int packRecd = (int)packRecdParmValue; int packSent = (int)packSentParmValue; int totalErrors = (int)totErrsParmValue; int totalRead = (int)totReadParmValue; int totalWrite = (int)totWriteParmValue; We also need to get the current time so we know when the statistics were taken, which we can get from the static Now property of the DateTime struct Since we ll be inserting this into an XML file, we must use the XSD format Fortunately, the DateTime.

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