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  2. Azure Data Explorer - Wikipedia

    en.wikipedia.org/wiki/Azure_Data_Explorer

    The development of the product began in 2014 as a grassroots incubation project in the Israeli R&D center of Microsoft, [12] with the internal code name 'Kusto' [9] [7] (named after Jacques Cousteau, as a reference to "exploring the ocean of data"). The project aim was to address Azure services' needs for fast and scalable log and telemetry ...

  3. Levenshtein distance - Wikipedia

    en.wikipedia.org/wiki/Levenshtein_distance

    In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.

  4. Primitive data type - Wikipedia

    en.wikipedia.org/wiki/Primitive_data_type

    Other languages such as JavaScript, Python, Ruby, and many dialects of BASIC do not have a primitive character type but instead add strings as a primitive data type, typically using the UTF-8 encoding. Strings with a length of one are normally used to represent single characters.

  5. Help:Advanced table formatting - Wikipedia

    en.wikipedia.org/wiki/Help:Advanced_table_formatting

    Edit-tricks are most useful when multiple tables must be changed, then the time needed to develop complex edit-patterns can be applied to each table. For each table, insert an alpha-prefix on each column (making each row-token "|-" to sort as column zero, like prefix "Row124col00"), then sort into a new file, and then de-prefix the column entries.

  6. Locality-sensitive hashing - Wikipedia

    en.wikipedia.org/wiki/Locality-sensitive_hashing

    In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.

  7. Bloom filter - Wikipedia

    en.wikipedia.org/wiki/Bloom_filter

    However, the main characteristic of SBFs is their ability to store multiple sets in a single data structure, which makes them suitable for a number of different application scenarios. [44]

  8. Abstract syntax tree - Wikipedia

    en.wikipedia.org/wiki/Abstract_syntax_tree

    The syntax is "abstract" in the sense that it does not represent every detail appearing in the real syntax, but rather just the structural or content-related details. For instance, grouping parentheses are implicit in the tree structure, so these do not have to be represented as separate nodes. Likewise, a syntactic construct like an if ...

  9. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text, where each line of the file typically represents one data record. Each record consists of the same number of fields, and these are separated by commas in the ...