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The UUID tool processes the databases and tries to correlate every account in each User Database to one or more potential owners in the Master Database.
The correlation is based on the fields defined for matching, weighted accordingly. The result is a Matching Configuration, where each of the users in the Master Database appears In the configuration's User Pane, and each of the users in the other User Databases representing accounts appear in the configuration's Resource.
The degree of match is represented in the score and appears as Res Name 3. This information is saved in the configuration file listed in the Output Config field of the Match Process section.
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.
Required Score to Match A value that represents the similarity required for a match. Example of Weight Match Rules Table displays the attribute values contained in two separate records that are read in the following order.
The Details tab is displayed at the bottom of the page. Select Add at the bottom of the page to add a new row. For each row, select an attribute to add to the rule using the Attribute column.
In Required score to match, assign an overall score for the match. Person Match Rules Built-in Person rules provide an easy and convenient way for matching names of individuals.
Person Roles Table describes the roles for different parts of a name that are used for matching. The "Mrs. Match" option is selected. First Name Standardized Compares the first names.
Last Name Compares the last names. Maturity Post Name Compares the post name, such as "Jr. Person Details Table describes the options that determine a match for Person match rules.
Match on initials Matches initials to names such as "R"' and "Robert". Match on substrings Matches substrings to names such as "Rob" to "Robert".
Similarity Score Records are considered a match if the similarity is greater than or equal to the score. Detect compound name Matches compound names to names such as "De Anne" to "Deanne".
Match hyphenated names Matches hyphenated names to unhyphenated names such as "Reese-Jones" to "Reese". The Person Attributes tab and Details tab are displayed at the bottom of the page.
For each attribute, select the role that it plays in a name. Firm Match Rules Built-in Firm match rules provide an easy and convenient way for matching business names.
Abhijeet Tiwari says:. Hello, I am trying to Merge two worksheet using this add-on but it is showing an error message as follows. Antonio Casas says:.
Thanks, A. Please Help, Thanks. Derrick Strom says:. Manish rao says:. Bill says:. Julia says:. Urban says:. Katerina Bespalaya says:.
Hi, I regret to tell you that the add-in can't be called via a macro. Please let me know if you have any other questions. Kay says:. Hello, I hope that you could be able to answer my question.
I hope that you could help me. Thanks in advance. Hello Kay, For us to be able to help you better, please send us a small sample workbook with your source data and the result you want to get to support ablebits.
SP says:. Can it automatically merge and combine two cells when one of the sheet cell value is added? Natalia Sharashova Ablebits.
Thank you for your question, Amanda. Max says:. Hi Max, Thank you for your question. Mary Trifuntova Ablebits. Hello Nasser, Thank you for contacting us.
Rob Jackson says:. Aksana Pachkouskaya Ablebits. 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.
To improve the matching, we want to apply several rules and associate a different confidence score to each of them:. Let's now add match rules that will leverage the normalized and phonetic names you added previously.
Now that new match rules have been defined to identify potential matches, we don't want all matches to merge automatically anymore.
To improve the matching, we will now add a new enricher which removes business entity type, and adjust the sequence of execution for enrichers as follows:.
This will result in an improved NormalizedName and will get your matching to a very interesting level. By default, the new enricher is executed in the last position.
Semarchy xDM tracks lots of additional metadata about the matches. Train your brain! Play the latest puzzle and brain games for kids online on vitalitygames.
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