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Figure 8.7 shows the sampling grid of an analysis with three voices per octave. Sampling the wavelet transform can be further generalized by choosing the sampling grid

Random Access Burst Random Access Channel Radio Frequency Channel Reduced TDMA Frame Number Radio Link Protocol Table of 128 integers in the hopping sequence Regular Pulse Excitation (Voice Codec) Received Signal Level Received Signal Quality Set Asynchronous Balanced Mode Slow Associated Control Channel Slow, SACCH/C4 Associated Control CHannel Slow, SACCH/C8 Associated Control CHannel Slow, TCH Associated Control CHannel Slow, TCH/F Associated Control CHannel Slow, TCH/H Associated Control CHannel Service Access Points Service Access Points Indicator Synchronization Burst Signalling Connection Control Part Synchronisation CHannel Sub Channel Number Standalone Dedicated Control CHannel Standalone Dedicated Control CHannel/4 Standalone Dedicated Control CHannel/8

want to serve network resources to local clients through an intranet. You can use TCP/IP as your only network protocol, or you can use it in conjunction with other protocols, such as NetBIOS.

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Term Extraction: Builds a new data flow based on terms it finds in a Unicode text column (DT_WSTR or DT_NTEXT) This is the training part of text mining, whereby strings of a particular type are used to generate a list of commonly used terms, which is later used by the Term Lookup component to identify similar strings For example, the text of saved RSS documents could be used to find similar documents in a large population Configure by identifying the column containing the Unicode text to be analyzed If a list of terms to be excluded has been built, identify the table and column on the Exclusions tab The Advanced tab controls the extraction algorithm, including whether terms are single words or phrases (articles, pronouns, etc, are never included), scoring algorithm, minimum frequency before extraction, and maximum phrase length.

Term Lookup: Provides a join between a Unicode text column in the data flow and a reference table of terms built by the Term Extraction component One row appears in the output data flow for each term matched The output data flow also contains two columns in addition to the selected input columns: Term and Frequency Term is the noun or noun phrase that was matched and Frequency is the number of occurrences in the data flow column Configure the transform by specifying the OLE DB connection manager and table that contains the list of terms Use the Term Lookup tab to check the input columns that should be passed through to the output data flow, and then map the input Unicode text column to the Term column of the reference table by dragging and dropping between those columns in the upper pane.

Figure 12-3: The bill of materials schema uses an adjacency table to store which parts are used to manufacture which other parts.

A complication created by referential integrity is that RI will prevent you from deleting or modifying a primary row being referred to by secondary rows until those secondary rows have been deleted If the primary row is deleted and the secondary rows foreign keys are still pointing to the now deleted primary keys, referential integrity is violated The solution to this problem is to modify the secondary rows as part of the primary table transaction DRI can do this automatically for you Four outcomes are possible for the affected secondary rows selected in the Delete Rule or Update Rule properties of the Foreign Key Relationships form Update Rule is meaningful for natural primary keys only: No Action: The secondary rows won t be modified in any way Their presence will block the primary rows from being deleted or modified.

Union All: Combines rows from multiple data flows into a single data flow, given the source columns are of compatible types Configure by connecting as many data flows as needed to the component Then, using the editor, ensure that the correct columns from each data flow are mapped to the appropriate output column Unpivot: Makes a data flow more normalized by making columns into attribute values For example, a data flow with one row for each quarter and a column for revenue by region could be turned into a three-column data flow: Quarter, Region, and Revenue..

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