An adaptive finite-state automata application to the problem of reducing the number of states in approximate string matching

This paper presents an alternative way to use finite-state automata in order to deal with approximate string matching. By exploring some adaptive features that enable any finitestate automaton model to change configuration during computational steps, dynamically deleting or creating new transitions, we can actually control the behavior and the topology of the automaton. We use these features for an application to approximate string matching trying to reduce the number of states requiredEje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

An adaptive finite-state automata application to the problem of reducing the number of states in approximate string matching

This paper presents an alternative way to use finite-state automata in order to deal with approximate string matching. By exploring some adaptive features that enable any finitestate automaton model to change configuration during computational steps, dynamically deleting or creating new transitions, we can actually control the behavior and the topology of the automaton. We use these features for an application to approximate string matching trying to reduce the number of states requiredEje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot