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Time to party! Move a sequence from the sequences list to Select Sequence. Use the Rank and Rank Record rules when merging data from multiple sources.
These rules enable you to identify your preference for certain sources. Your data must have a second input attribute on which the rule is based.
For example, the second attribute might identify the data source, and these data sources are ranked in order of reliability.
The most reliable value would be used in the merged record. The merge rule might look like this:. An arbitrary name for the rule.
You can replace these names with meaningful ones. The row headers are the boxes to the left of the Name column.
The custom SQL expression used in the ranking. Use this editor to develop the ranking expression. The Min Max and Min Max Record rules select an attribute value based on the size of another attribute value in the record.
For example, you might select the First Name value from the record in each bin that contains the longest Last Name value. Selects the largest numeric value or the most recent date value.
You can edit code directly in this field or use the Custom Merge Rule Editor. Use the Match Merge operator to identify matching records in a data source and to merge them into a single record.
The Match Merge operator has one input group and two output groups, Merge and Xref. The source data is mapped to the input group.
The Merge group contains records that have been merged after the matching process is complete. The Xref group provides a record of the merge process.
Every record in the input group will have a corresponding record in the Xref group. This record may contain the original attribute values and the merged attributes.
The Match Merge operator uses an ordered record stream as input. From this stream, it constructs the match bins. From each match bin, matched sets are constructed.
From each matched set, a merged record is created. Drag and drop the operators representing the source data and the operator representing the merged data onto the Mapping Editor canvas.
For example, if your source data is stored in a table, and the merged data will be stored in another table, drag and drop two Table operators that are bound to the tables onto the canvas.
On the Name page, the Name field contains a default name for the operator. You can change this name or accept the default name. XREF: Contains the link between the original and merged data sets.
This is the tracking mechanism used when a merge is performed. On the Input Connections page, move the attributes that you want to match and merge from the Available Attributes section to the Mapped Attributes section.
Click Next. The Available Attributes section of this page displays nodes for each operator on the canvas. Expand a node to display the attributes contained in the operator, select the attributes, and use the shuttle arrows to move selected attributes to the Mapped Attributes section.
In general, if you go through the wizard, you need not change any of these values. Warehouse Builder populates them based on the output attributes.
These attributes appear in the Merge output group the cleansed group. The attributes in this group retain the name and properties of the input attributes.
The Source Attributes section contains all the input attributes and the Merge attributes that you selected on the Merge Output page. The other attributes define the unmodified input attribute values.
Select at least one attribute from the Merge group that will provide a link between the input and Merge groups. On the Match Bins page, specify the match bin attributes.
These attributes are used to group source data into match bins. After the first deployment, you can choose whether to match and merge all records or only new records.
To match and merge only the new records, select Match New Records Only. You must designate a condition that identifies new records.
The Match Merge operator treats the new records in the following way:. No matching is performed for any records in a match bin unless the match bin contains a new record.
A matched record set is not presented to the merge processing unless the matched record set contains a new record.
For more information about match bin attributes and match bins, see "Overview of the Matching and Merging Process". On the Define Match Rules page, define the match rules that will be used to match the source data.
A passive match rule is generated but not automatically invoked. You must define at least one active match rule.
For more information about the match rules, the types of match rules that you can define, and the steps used to define them, see "Match Rules".
On the Merge Rules page, define the rules that will be used to merge the sets of matched records created from the source data.
You can define Merge rules for each attribute in a record or for the entire record. Warehouse Builder provides different types of Merge rules.
For more information about the types of Merge rules and the steps to create Merge rules, see "Merge Rules". On the Summary page, review your selections.
Click Back to modify any selection that you made. Click Next to complete creating the Match Merge operator. Map the Merge group of the Match Merge operator to the input group of the operator that stores the merged data.
Operating modes: A mapping that contains a Match Merge operator can only run in set-based mode. Operators may accept either set-based or row-based input and generate either set-based or row-based output.
SQL is set-based, so a set of records is processed at one time. When the Match Merge operator matches records, it compares each row with the subsequent row in the source, and generates row-based code only.
If set-based operators appear after Match Merge operator, then the mapping is invalid. If you need to process the output of a match-merge mapping using a set-based SQL operator, then stage the output in an intermediate table.
You must use a staging table. Most match-merge operations can be performed by a single Match Merge operator.
However, if you are directing the output to two different targets, then you may need to use two Match Merge operators in succession. For example, when householding name and address data, you may need to merge the data first for addresses and then again for names.
Figure shows a mapping that uses two Match Merge operators. Skip Headers. Warehouse Builder matching and merging provides the following functionality: Determine matches using built-in algorithms, such as the Jaro-Winkler and Levenshtein edit distance algorithms, or using a custom algorithm you implement.
Use weighting to determine matches between records. Generate a table containing candidate matches, as input to some other merge logic, such as an existing master data management application Generate a table with merged data records, with merge logic based on built-in merge rules, custom-implemented merge logic, or complex merge rules that can combine packaged and custom rules Cross reference data to track and audit matches.
Built-in advanced matching rules for person, firm and address data Warehouse Builder matching and merging can be combined with Warehouse Builder name and address cleansing functionality to support householding , which is the process of identifying unique households in name and address data.
This improves the quality of your results, and can improve performance because cleansed rows are more easily identified as matches Figure Match Merge Operator in a Mapping Description of "Figure Match Merge Operator in a Mapping".
Overview of the Matching and Merging Process Matching determines which records refer to the same logical data. Elements of Matching and Merging Records The following concepts and terms are important in understanding the matching and merging process.
Select match bin attributes carefully to fulfill the following two conflicting needs: Ensure that any records that could match reside in the same match bin.
Keep the size of the match bin as small as possible. The high-level tasks involved in matching and merging process include the following: Constructing Match Bins Constructing Match Record Sets Constructing Merge Records Figure represents high-level tasks involved in the matching and merging process.
Constructing Match Bins The match bin is constructed using the match bin attributes. Constructing Match Record Sets Match rules are applied to all the records in each match bin to generate one or more match record sets.
See Also: "Match Rules" for information about the types of match rules and how to create them. Constructing Merge Records A single merge record is constructed from each match record set.
See Also: "Merge Rules" for more information about the types of merge rules. Match Rules Match rules are used to determine if two records are logically similar.
Table describes the types of match rules. No rows match within the match bin. Conditional Matches rows based on the algorithm you set.
Weight Matches rows based on scores that you assign to the attributes. Person Matches records based on the names of people.
Firm Matches records based on the name of the organization or firm. Address Matches records based on postal addresses. Custom Matches records based on a custom comparison algorithm that you define.
Conditional Match Rules Conditional match rules specify the conditions under which records match.
You can specify how attributes are compared using comparison algorithms. Attribute Identifies the attribute that will be tested for a particular condition.
Position The order of execution. Algorithm A list of methods that can be used to determine a match. Blank Matching Lists options for handling empty strings in a match.
Comparison Algorithms Each attribute in a conditional match rule is assigned a comparison algorithm, which specifies how the attribute values are compared.
Table describes the types of comparisons. Standardized Exact Standardizes the values of the attributes before comparing them for an exact match.
Soundex Converts the data to a Soundex representation and then compares the text strings. Edit Distance A "similarity score" in the range 0 to is entered.
The algorithm used here is the Levenshtein edit distance algorithm. Standardized Edit Distance Standardizes the values of the attribute before using the Similarity algorithm to determine a match.
Partial Name The values of a string attribute are considered a match if the value of one entire attribute is contained within the other, starting with the first word.
Abbreviation The values of a string attribute are considered a match if one string contains words that are abbreviations of corresponding words in the other.
For example, "Intl. Business Products" would match "International Bus Prd". Acronym The values of a string attribute are considered a match if one string is an acronym for the other.
Jaro-Winkler Matches strings based on their similarity value using an improved comparison system over the Edit Distance algorithm.
Standardized Jaro-Winkler Eliminates case, spaces, and nonalphanumeric characters before using the Jaro-Winkler algorithm to determine a match.
Double Metaphone Matches phonetically similar strings using an improved coding system over the Soundex algorithm.
A Details section is displayed. Click Add to add a new row. Select an attribute in the Attribute column.
Select a method for handling blanks. Match Rules: Basic Example The following discussions illustrate how some basic match rules apply to real data and how multiple match rules can interact with each other.
Example: How Multiple Match Rules Combine If you create more than one match rule, Warehouse Builder determines two rows match if those rows satisfy any of the match rules.
The following example illustrates how Warehouse Builder evaluates multiple match rules. Weight Match Rules A weighted match rule enables you to assign an integer weight to each attribute included in the rule.
Similarity Algorithm The method used to determine a match. Choose from these algorithms: Edit Distance: Calculates the number of deletions, insertions, or substitutions required to transform one string into another.
Maximum Score The weight value for the attribute. Score When Blank The similarity value when one of the records is empty.
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Train your brain! This is a key success factor for matching as it allows matching on cleaner, richer, and more standardized data. In this first step, you will be guided to create a SemQL enricher to standardize company names.
Before setting up an enricher, you first need to add an attribute to the Company entity to store standardized company names.
The goal is to remove punctuation. Reloading data is required to execute the enricher on all existing data. In this section, you will delete and load data again using the xDM user interface.
If you're loading from a data integration tool, you may use it to truncate and reload data. This is a very common special case in match rules.
Let's start by adding an attribute to store the phonetized name. Proceed the same way you did for NormalizedName. This is an extremely useful technique depending on where your source errors come from.
You now need to reload data to see the improved name standardization and the name phonetization. Repeat the same operations as previously:.
Semarchy xDM provides a powerful mechanism to define multiple match rules with different match scores, and merge policies to define what happens to clusters of potential matches as they become golden records.First Name Standardized Compares the first names. No Deposit Casino Coupon Codes this colorful puzzle game, you must Bubbles Shooter Games blocks to create bigger blocks and then even bigger blocks. Match on abbreviations Uses the Abbreviation algorithm to determine a match. Assign a conditional match rule based on similarity such as described in Table The Required Score to Match is See Table for the types of roles that you can assign. Uses the Acronym algorithm to determine a match. Jim Hargis says:. Book List. In order we could provide you with the most accurate feedback, please contact our support team at support ablebits. With this unit, Gute Free To Play Mmorpg learned to enrich, match and merge data within your first data consolidation application.