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Compressed suffix arrays are a general class of data structure that improve on the suffix array. [1] [2] These data structures enable quick search for an arbitrary string with a comparatively small index. Given a text T of n characters from an alphabet Σ, a compressed suffix array supports searching for arbitrary patterns in T.
There are ports and bindings in various languages including Java, C#, Rust, and Python. [8] The Apache Hadoop system uses this algorithm for fast compression. LZ4 was also implemented natively in the Linux kernel 3.11. [9]
Snappy (previously known as Zippy) is a fast data compression and decompression library written in C++ by Google based on ideas from LZ77 and open-sourced in 2011. [3] [4] It does not aim for maximum compression, or compatibility with any other compression library; instead, it aims for very high speeds and reasonable compression.
As an example consider the sequence of tokens AABBA which would assemble the dictionary; 0 {0,_} 1 {0,A} 2 {1,B} 3 {0,B} and the output sequence of the compressed data would be 0A1B0B. Note that the last A is not represented yet as the algorithm cannot know what comes next. In practice an EOF marker is added to the input – AABBA$ for
Java bytecode is the instruction set of the Java virtual machine (JVM), the language to which Java and other JVM-compatible source code is compiled. [1] Each instruction is represented by a single byte , hence the name bytecode , making it a compact form of data .
literal_bit_mode is an array of 8 values in the 0–2 range, one for each bit position in a byte, which are 1 or 2 if the previous packet was a *MATCH and it is either the most significant bit position or all the more significant bits in the literal to encode/decode are equal to the bits in the corresponding positions in match_byte, while ...
Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
For example, to perform an element by element sum of two arrays, a and b to produce a third c, it is only necessary to write c = a + b In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x)