Name
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. We propose a unified framework for frequently evaluating existing FA-based string recognizers such that FA-implementers could capture appropriate problem domains that guarantee an optimal performance of available recognizers. The suggested framework takes into consideration factors such as the kind of automaton being processed, the string and alphabet size as well as the overall behaviour of the automaton at run-time. It also forms the basis for further work on FA-based string recognition applications in specific computational domains such as natural language processing, computational biology, natural and computer virus scanning, network intrusion detection, etc. It is well-known that performance remains a significant bottleneck to the high-performance solutions required in such industrial applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1899503.1899528 | SAICSIT Conf. |
Keywords | Field | DocType |
suggested algorithm,fast string recognition,fa-based string recognizers,fa-based string recognition application,suggested framework,existing fa-based string,computational biology,domain-specific fa-implementers,available recognizers,optimal performance,computer science,automata,computer virus,algorithms,natural language processing | Bottleneck,Network intrusion detection,Computer science,Computer virus,Automaton,Theoretical computer science,Implementation,Artificial intelligence,Machine learning,Benchmarking,Alphabet | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ernest Ketcha Ngassam | 1 | 17 | 4.66 |
| Derrick G. Kourie | 2 | 223 | 33.10 |
| Bruce W. Watson | 3 | 338 | 53.24 |