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.

Polynomial-time inference of arbitrary pattern languages

Abstract: A pattern is a finite string of constants and variables (cf. [1]). The language of a pattern is the set of all strings which can be obtained by substituting non-null strings of constants for the variables of the pattern. In the present paper, we consider the problem of learning pattern languages from examples. As a main result we present an inconsistent polynomial-time algorithm which identifies every pattern language in the limit. Furthermore, we investigate inference of arbitrary pattern languages within the framework of learning from good examples. Finally, we show that every pattern language can be identified in polynomial time from polynomially many disjointness queries, only.

Publicly Verifiable Non-Interactive Zero-Knowledge Proofs

Abstract: In this paper we construct the first publicly verifiable non-interactive zero-knowledge proof for any NP statement under the general assumption that one way permutations exist. If the prover is polynomially bounded then our scheme is based on the stronger assumption that trapdoor permutations exist. In both cases we assume that P and V have a common random string, and use it to prove a single theorem (which may be chosen as a function of the known string).

Improved Dynamic Dictionary Matching

Abstract: In the dynamic dictionary matching problem, a dictionary D contains a set of patterns that can change over time by insertion and deletion of individual patterns. The user also presents text strings and asks for all occurrences of any patterns in the text. The two main contributions of this paper are: (1) a faster algorithm for dynamic string dictionary matching with bounded alphabets, and (2) a dynamic dictionary matching algorithm for two-dimensional texts and patterns. The first contribution is based on an algorithm that solves the general problem of maintaining a sequence of well-balanced parentheses under the operations insert, delete, and find nearest enclosing parenthesis pair. The main new idea behind the second contribution is a novel method to efficiently manipulate failure links for two-dimensional patterns.

A Memory-Efficient Bit-Split Parallel String Matching Using Pattern Dividing for Intrusion Detection Systems

Abstract: For the low-cost hardware-based intrusion detection systems, this paper proposes a memory-efficient parallel string matching scheme. In order to reduce the number of state transitions, the finite state machine tiles in a string matcher adopt bit-level input symbols. Long target patterns are divided into subpatterns with a fixed length; deterministic finite automata are built with the subpatterns. Using the pattern dividing, the variety of target pattern lengths can be mitigated, so that memory usage in homogeneous string matchers can be efficient. In order to identify each original long pattern being divided, a two-stage sequential matching scheme is proposed for the successive matches with subpatterns. Experimental results show that total memory requirements decrease on average by 47.8 percent and 62.8 percent for Snort and ClamAV rule sets, in comparison with several existing bit-split string matching methods.

Flexible string matching against large databases in practice

Abstract: Data Cleaning is an important process that has been at the center of research interest in recent years. Poor data quality is the result of a variety of reasons, including data entry errors and multiple conventions for recording database fields, and has a significant impact on a variety of business issues. Hence, there is a pressing need for technologies that enable flexible (fuzzy) matching of string information in a database. Cosine similarity with tf-idf is a well-established metric for comparing text, and recent proposals have adapted this similarity measure for flexibly matching a query string with values in a single attribute of a relation. In deploying tf-idf based flexible string matching against real AT&T databases, we observed that this technique needed to be enhanced in many ways. First, along the functionality dimension, where there was a need to flexibly match along multiple string-valued attributes, and also take advantage of known semantic equivalences. Second, we identified various performance enhancements to speed up the matching process, potentially trading off a small degree of accuracy for substantial performance gains. In this paper, we report on our techniques and experience in dealing with flexible string matching against real AT&T databases.