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Record linkage (also known as data matching, data linkage, entity resolution, and many other terms) is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).
The process can be performed based on algorithms or programmed loops. Trying to match sets of data against each other or comparing complex data types. Data matching is used to remove duplicate records and identify links between two data sets for marketing, security or other uses. [3] Sounds like Function is used to find values that sound similar.
For data requests that fall between the table's samples, an interpolation algorithm can generate reasonable approximations by averaging nearby samples." [8] In data analysis applications, such as image processing, a lookup table (LUT) can be used to transform the input data into a more desirable output format. For example, a grayscale picture ...
Common applications of approximate matching include spell checking. [5] With the availability of large amounts of DNA data, matching of nucleotide sequences has become an important application. [1] Approximate matching is also used in spam filtering. [5] Record linkage is a common application where records from two disparate databases are matched.
Calculations were performed on these objects, as opposed to a range of cells, so adding two-time series automatically aligns them in calendar time, or in a user-defined time frame. Data were independent of worksheets—variables, and therefore data, could not be destroyed by deleting a row, column, or entire worksheet.
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The participants on one side of the matching (the hospitals) may have a numerical capacity, specifying the number of doctors they are willing to hire. The total number of participants on one side might not equal the total capacity to which they are to be matched on the other side. The resulting matching might not match all of the participants.
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