Framework for benchmarking FA-based string recognizers

Abstract

Previous work on implementations of FA-based string recognizers suggested a range of implementation strategies (and therefore, algorithms) aiming at improving their performance for fast string recognition. However, an efficient exploitation of suggested algorithms by domain-specific FA-implementers requires prior knowledge of the behaviour (performance-wise) of each algorithm in order to make an informed choice. The authors propose a based string recognizers such that FA-implementers could capture appropriate problem domains that gaurantee an optimal performance of available recognizers. The suggested framework takes into consideration factors such as the kind of automan being processed, the string and alphabet size as well as the overall behaviour of the automan at run-time. It is well known that performance remains a significant bottle-neck to the high-performance solutions required in such industrial applications.