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Set-Membership constraints: The values for a column come from a set of discrete values or codes. For example, a person's sex may be Female, Male or Non-Binary. Foreign-key constraints: This is the more general case of set membership. The set of values in a column is defined in a column of another table that contains unique values.
Add the template to the table caption, and then only screen reader users will see it. Example: |+ {{sro|Table caption}} For current table caption and summary guidelines see the w3.org page: Caption & Summary, in Tables Tutorial. Web Accessibility Initiative (WAI). The summary info in the paragraph below is out of date:
CSV is a delimited text file that uses a comma to separate values (many implementations of CSV import/export tools allow other separators to be used; for example, the use of a "Sep=^" row as the first row in the *.csv file will cause Excel to open the file expecting caret "^" to be the separator instead of comma ","). Simple CSV implementations ...
The two most common representations are column-oriented (columnar format) and row-oriented (row format). [ 1 ] [ 2 ] The choice of data orientation is a trade-off and an architectural decision in databases , query engines, and numerical simulations. [ 1 ]
First normal form (1NF) is a property of a relation in a relational database. A relation is in first normal form if and only if no attribute domain has relations as elements. [1] Or more informally, that no table column can have tables as values.
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For example, a database that represents company contact information might have the following columns: ID, Company Name, Address Line 1, Address Line 2, City, and Postal Code. More formally, a row is a tuple containing a specific value for each column, [ 4 ] for example: (1234, 'Big Company Inc.', '123 East Example Street', '456 West Example ...
Most lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that "probable" (i.e. frequently encountered) data will produce shorter output than "improbable" data.