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In 493 AD, Victorius of Aquitaine wrote a 98-column multiplication table which gave (in Roman numerals) the product of every number from 2 to 50 times and the rows were "a list of numbers starting with one thousand, descending by hundreds to one hundred, then descending by tens to ten, then by ones to one, and then the fractions down to 1/144 ...
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
A regex search scans the text of each page on Wikipedia in real time, character by character, to find pages that match a specific sequence or pattern of characters. Unlike keyword searching, regex searching is by default case-sensitive, does not ignore punctuation, and operates directly on the page source (MediaWiki markup) rather than on the ...
Suppose a database contains N data items and one must be retrieved based on the value of one of the fields. A simple implementation retrieves and examines each item according to the test. If there is only one matching item, this can stop when it finds that single item, but if there are multiple matches, it must test everything.
In SQL, wildcard characters can be used in LIKE expressions; the percent sign % matches zero or more characters, and underscore _ a single character. Transact-SQL also supports square brackets ([and ]) to list sets and ranges of characters to match, a leading caret ^ negates the set and matches only a character not within the list.
The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot
Sorting or ordering the data based on a list of columns to improve search performance; 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
If the characters do not match, there is no need to continue searching backwards along the text. If the character in the text does not match any of the characters in the pattern, then the next character in the text to check is located m characters farther along the text, where m is the length of the pattern.