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Lookup tables are also used extensively to validate input values by matching against a list of valid (or invalid) items in an array and, in some programming languages, may include pointer functions (or offsets to labels) to process the matching input.
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. String matching cannot be used for most binary data, such as images ...
Microsoft Excel is a spreadsheet editor developed by Microsoft for Windows, macOS, Android, iOS and iPadOS.It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA).
One can then test whether a key is present in S, or look up a value associated with that key, by looking for it at its cell of the table. Each such lookup takes constant time in the worst case. [2] With perfect hashing, the associated data can be read or written with a single access to the table. [3]
Edit distance finds applications in computational biology and natural language processing, e.g. the correction of spelling mistakes or OCR errors, and approximate string matching, where the objective is to find matches for short strings in many longer texts, in situations where a small number of differences is to be expected.
Decision tables can be, and often are, embedded within computer programs and used to "drive" the logic of the program. A simple example might be a lookup table containing a range of possible input values and a function pointer to the section of code to process that input.
If the phone numbers are indexed, the same search would occur in the smaller index table, gathering the keys of matching records, and then looking in the main data table for the records with those keys. Usually, a table is stored in a way that allows a lookup via a key to be very fast. [20]
Joining data from multiple sources (e.g., lookup, merge) and deduplicating the data; Aggregating (for example, rollup – summarizing multiple rows of data – total sales for each store, and for each region, etc.) Generating surrogate-key values; Transposing or pivoting (turning multiple columns into multiple rows or vice versa)