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Standard examples of data-driven languages are the text-processing languages sed and AWK, [1] and the document transformation language XSLT, where the data is a sequence of lines in an input stream – these are thus also known as line-oriented languages – and pattern matching is primarily done via regular expressions or line numbers.
The general problem of matching any number of backreferences is NP-complete, and the execution time for known algorithms grows exponentially by the number of backreference groups used. [ 45 ] However, many tools, libraries, and engines that provide such constructions still use the term regular expression for their patterns.
Tree patterns are used in some programming languages as a general tool to process data based on its structure, e.g. C#, [1] F#, [2] Haskell, [3] Java [4], ML, Python, [5] Ruby, [6] Rust, [7] Scala, [8] Swift [9] and the symbolic mathematics language Mathematica have special syntax for expressing tree patterns and a language construct for ...
an atom is either a number or a symbol; a number is an unbroken sequence of one or more decimal digits, optionally preceded by a plus or minus sign; a symbol is a letter followed by zero or more of any characters (excluding whitespace); and; a list is a matched pair of parentheses, with zero or more expressions inside it.
The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [394] ADGEfficiency Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [395] University of Zurich GreenBiz
A stylistic depiction of values inside of a so-named comma-separated values (CSV) text file. The commas (shown in red) are used as field delimiters. A delimiter is a sequence of one or more characters for specifying the boundary between separate, independent regions in plain text, mathematical expressions or other data streams.
For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)