String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Computer system and method for converting a conversational statement to computer command language

Abstract: An input device (12) receives a natural language or conversational statement in the form of a character string (1). A processor (14) performs a natural language analysis (2) to convert the input natural language into a command language instruction for a computer program. A morphological analysis (3) compares words of the input character string with contents of a dictionary (10) to convert the input words into preselected words indicated by the dictionary which are output as another character string. In a semantic or syntax analysis (4; FIG. 9), one of the inputted and another character strings are analyzed to generate a corresponding chained functions structure (FIGS. 2, 3). From knowledge (FIG. 5, 7) described by the plurality of chained function structures and from rules stored in knowledge memory (11), a new character string is generated. If the new character string is in command language, the command is executed (7). If the new character string is not in command language, it is reanalyzed (8, 9) to generate yet another character string. In this manner, instructions can be input by a user in any of a multiplicity of national languages in conversational format and converted into appropriate command instructions for an executed computer program.

Dictionary-based order-preserving string compression

Abstract: As no database exists without indexes, no index implementation exists without order-preserving key compression, in particular, without prefix and tail compression. However, despite the great potentials of making indexes smaller and faster, application of general compression methods to ordered data sets has advanced very little. This paper demonstrates that the fast dictionary-based methods can be applied to order-preserving compression almost with the same freedom as in the general case. The proposed new technology has the same speed and a compression rate only marginally lower than the traditional order-indifferent dictionary encoding. Procedures for encoding and generating the encode tables are described covering such order-related features as ordered data set restrictions, sensitivity and insensitivity to a character position, and one-symbol encoding of each frequent trailing character sequence. The experimental results presented demonstrate five-folded compression on real-life data sets and twelve-folded compression on Wisconsin benchmark text fields.

Apparatus and method for handling string operations in a pipelined processor

Abstract: In a pipelined processor, an apparatus for handling string operations. When a string operation is received by the processor, the length of the string as specified by the programmer is stored in a register. Next, an instruction sequencer issues an instruction that computes the register value minus a pre-determined number of iterations to be issued into the pipeline. Following the instruction, the pre-determined number of iterations are issued to the pipeline. When the instruction returns with the calculated number, the instruction sequencer then knows exactly how many iterations should be executed. Any extra iterations that had initially been issued are canceled by the execution unit, and additional iterations are issued as necessary. A loop counter in the instruction sequencer is used to track the number of iterations.

A polynomial-time algorithm for learning k-variable pattern languages from examples

Abstract: This chapter focuses on a polynomial-time algorithm for learning k -variable pattern languages in the learning model introduced by Valiant for each constant k . A pattern is a string of constant and variable symbols. For any constant k , the algorithm learns a k -variable target pattern p by producing a polynomial-sized disjunction of patterns, each of between 0 and k variables. The algorithm allows empty substitutions and can be extended to handle restricted homomorphisms on the substitution strings. It is assumed that the algorithm has access to a random source of negative examples, generated according to an arbitrary distribution, and a random source of positive examples of the target pattern p in which the k -tuple of substitution strings is drawn not from an arbitrary distribution but from any product distribution.

A Fully Linear-Time Approximation Algorithm for Grammar-Based Compression

Abstract: A linear-time approximation algorithm for the grammar-based compression is presented. This is an optimization problem to minimize the size of a context-free grammar deriving a given string. For each string of length n , the algorithm guarantees O ( log n g ∗ ) approximation ratio without suffix tree construction.