![]() The result is computed as percentage:įrom 0 (not equal) to 100 (exactly equal). Is computed as a percentage: 0 (not equal) to 100 (equal).Įdit_distance: A way of quantifying how dissimilar two stringsĪre by counting the minimum number of operations required to transform Comparison functionsįusion supports the following comparison functions:Įqual: Exact equality. The more blocking records the better, up to limit. Thus it is recommended that in each match rule you include one or more columns that will block your records. The matching process searches for matches only withinĪ subset. Records are preliminarily grouped into subsets according to the values in their blocking columns, which means each record is contained in one and only one subset - the subsets are mutually exclusive and collectively exhaustive. Blocking columns are columns that use the same comparison functions. To avoid all-pairsĬomparisons, Fusion uses a blocking technique. The Weight Histogram Table contains two columns:Īll record pairs in large tables because the number of possible pairs is prohibitive. To select the match cutoff value you should review create a Weight Histogram Table by running the match run in the determineĬutoffs mode. You can specify the comparison functions used. Record pairs that have a composite score equal to or greater than the matchĬutoff value are declared to be matches. ![]() All pairs of recordsĪre compared, a composite score is computed for each pair of records, and Only one match cutoff value is specified. Values in columns, without you specifying the exact set of columns that This type of matching allows you to match records by comparing the Match Rule Type: Fuzzy, combined single limit To be declared a match, all match columns must be a match. If the score is greater orĮqual to the match cutoff value, the column is declared to be a match. The score returned by the column's comparison function is compared with the column's This type of matching allows you to specify the match cutoff and comparison function for each match column. The Exact type of match rule is simple, yet very powerful, particularly if you standardize your columns first. Fusion performs data matching by running a matching specification that contains one or more match rules that areįusion supports the following types of match rules:įor this type of rule, records that have equal values in the match columns are considered duplicates. Match rules define the conditions that must exist for records to be declared duplicates. Survivorship function: For a set of matched records, a survivorship function is used to determine which record's value will be used in the final merged record. Match cutoff: If the comparison of two values or records generates a score greater than or equal to the match cutoff value, the values or records are declared a match. This value is specified as a percentage ranging from 0 to 100. (See Comparison Functions.)Ĭomposite score: The composite score of comparing two records is computed as a sum of the column comparisons scores for the given columns, divided by the number of columns. Fusion provides several comparison functions that allow the implementation of fuzzy matching. ![]() Match column: A column used in the matching process to determine if records are duplicates.Ĭomparison function: A comparison function compares a column's value in two records and determines the likelihood that the values match. Records into a single consolidated record. Same real-world entity (duplicate records) and merging the identified How Data Matching for Deduplication (One Source Matching) Worksįusion's one source matching functionality automates the process of identifying records that represent the ![]()
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