Regular Expression Pattern Matching Research Articles

Research on Complex Event Detection over Streams Supporting Interval Temporal Logic and Regular Expression Pattern Matching

To improve the detective ability of complex event processing (CEP), we combine interval temporal logic (ITL) and regular expression in pattern description of event streams constraint. We propose different timed automata to support varied ITL. The pattern described by regular expression improves the simplicity of the pattern description. Meanwhile, we have implemented above technique in a CEP system to test performance. The experimental evaluation shows that the pattern can be efficiently and accurately matched.

Rancang Bangun Aplikasi Chatbot Sebagai Media Pencarian Informasi Anime Menggunakan Regular Expression Pattern Matching

Technology development has affected many areas of life, especially the entertainment field. One of the fastest growing entertainment industry is anime. Anime has evolved as a trend and a hobby, especially for the population in the regions of Asia. The number of anime fans grow every year and trying to dig up as much information about their favorite anime. Therefore, a chatbot application was developed in this study as anime information retrieval media using regular expression pattern matching method. This application is intended to facilitate the anime fans in searching for information about the anime they like. By using this application, user can gain a convenience and interactive anime data retrieval that can’t be found when searching for information via search engines. Chatbot application has successfully met the standards of information retrieval engine with a very good results, the value of 72% precision and 100% recall showing the harmonic mean of 83.7%. As the application of hedonic, chatbot already influencing Behavioral Intention to Use by 83% and Immersion by 82%.
 Index Terms—anime, chatbot, information retrieval, Natural Language Processing (NLP), Regular Expression Pattern Matching

Computer-Aided Word Mastermind Solving Using Regular Expression Pattern Matching in a Directed Acyclic Word Graph

Computer-Aided Word Mastermind Solving Using Regular Expression Pattern Matching in a Directed Acyclic Word Graph

GPU-based NFA implementation for memory efficient high speed regular expression matching

Regular expression pattern matching is the foundation and core engine of many network functions, such as network intrusion detection, worm detection, traffic analysis, web applications and so on. DFA-based solutions suffer exponentially exploding state space and cannot be remedied without sacrificing matching speed. Given this scalability problem of DFA-based methods, there has been increasing interest in NFA-based methods for memory efficient regular expression matching. To achieve high matching speed using NFA, it requires potentially massive parallel processing, and hence represents an ideal programming task on Graphic Processor Unit (GPU). Based on in-depth understanding of NFA properties as well as GPU architecture, we propose effective methods for fitting NFAs into GPU architecture through proper data structure and parallel programming design, so that GPU's parallel processing power can be better utilized to achieve high speed regular expression matching. Experiment results demonstrate that, compared with the existing GPU-based NFA implementation method [9], our proposed methods can boost matching speed by 29~46 times, consistently yielding above 10Gbps matching speed on NVIDIA GTX-460 GPU. Meanwhile, our design only needs a small amount of memory space, growing exponentially more slowly than DFA size. These results make our design an effective solution for memory efficient high speed regular expression matching, and clearly demonstrate the power and potential of GPU as a platform for memory efficient high speed regular expression matching.

MEASURING INCONSISTENCY IN A NETWORK INTRUSION DETECTION RULE SET BASED ON SNORT

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

INFAnt

This paper presents iNFAnt, a parallel engine for regular expression pattern matching. In contrast with traditional approaches, iNFAnt adopts non-deterministic automata, allowing the compilation of very large and complex rule sets that are otherwise hard to treat. iNFAnt is explicitly designed and developed to run on graphical processing units that provide large amounts of concurrent threads; this parallelism is exploited to handle the non-determinism of the model and to process multiple packets at once, thus achieving high performance levels.

Regular expression filters for XML

XML data are described by types involving regular expressions. This raises the question of what language feature is convenient for manipulating such data. Previously, we have given an answer to this question by proposing regular expression pattern matching. However, since this construct is derived from ML pattern matching, it does not have an iteration functionality in itself, which makes it cumbersome to process data typed by Kleene stars. In this paper, we propose a novel programming feature regular expression filters. This construct extends the previous proposal by permitting pattern clauses to be closed under arbitrary regular expression operators. This yields many convenient programming idioms such as non-uniform processing of sequences and almost-copying of trees. We further develop a type inference mechanism that obtains (1) types for pattern variables that are locally precise with respect to the type of input values and (2) a type for the result of the whole filter expression that is also locally precise with respect to the types of the body expressions. We discuss how our construct is useful in the practice of XML processing and, in particular, how our type inference is crucial for avoiding changes of programs when types of data to be processed evolve frequently.

Dynamic visual data mining: biological sequence analysis and annotation using SeqVISTA

In the post-genomic era, the volume of public sequence databases is increasing exponentially and visualisation-centric techniques have become more and more important in biological sequence analysis and annotation. In this paper, we present a methodology called dynamic visual data mining (DVDM), which combines biological object modelling, interactive display, and data analysis tools into one integrative platform. Using Java Development Kit v1.4, an object-oriented software named SeqVISTA has been developed based on DVDM. To illustrate the application of SeqVISTA, the following examples are shown: regular expression pattern matching; comparative analysis of alternative exon splicing patterns; Fourier analyses; exon prediction (MZEF and GENSCAN). Overall, we argue that DVDM is an important technique for biologists to unveil the information hidden behind the large genomic and proteomic databases, and SeqVISTA provides a versatile tool that integrates multiple computational algorithms for meeting biologists' data mining needs.

Generating Optimal Monitors for Extended Regular Expressions

Ordinary software engineers and programmers can easily understand regular patterns, as shown by the immense interest in and the success of scripting languages like Perl, based essentially on regular expression pattern matching. We believe that regular expressions provide an elegant and powerful specification language also for monitoring requirements, because an execution trace of a program is in fact a string of states. Extended regular expressions (EREs) add complementation to regular expressions, which brings additional benefits by allowing one to specify patterns that must not occur during an execution. Complementation gives one the power to express patterns on strings more compactly. In this paper we present a technique to generate optimal monitors from EREs. Our monitors are deterministic finite automata (DFA) and our novel contribution is to generate them using a modern coalgebraic technique called coinduction. Based on experiments with our implementation, which can be publicly tested and used over the web, we believe that our technique is more efficient than the simplistic method based on complementation of automata which can quickly lead to a highly-exponential state explosion.

A Boyer–Moore-style algorithm for regular expression pattern matching

This paper presents a Boyer–Moore-type algorithm for regular expression pattern matching, answering an open problem posed by Aho in 1980 (Pattern Matching in Strings, Academic Press, New York, 1980, p. 342). The new algorithm handles patterns specified by regular expressions—a generalization of the Boyer–Moore and Commentz-Walter algorithms. Like the Boyer–Moore and Commentz-Walter algorithms, the new algorithm makes use of shift functions which can be precomputed and tabulated. The precomputation algorithms are derived, and it is shown that the required shift functions can be precomputed from Commentz-Walter's d 1 and d 2 shift functions. In certain cases, the Boyer–Moore (respectively Commentz-Walter) algorithm has greatly outperformed the Knuth–Morris–Pratt (respectively Aho–Corasick) algorithm (as discussed by Watson in his Ph.D. Thesis, Eindhoven University of Technology, September 1995, and in: N. Ziviani, R. Baeza-Yates, K. Guimaraes (Eds.), Proc. Third South American Workshop on String Processing, International Informatics Series, vol. 4, Carleton University Press, Recife, Brazil, 1996, pp. 280–294). In testing, the algorithm presented in this paper also frequently outperforms the regular expression generalization of the Aho–Corasick algorithm.

Kangaroo – A pattern-matching program for biological sequences

BackgroundBiologists are often interested in performing a simple database search to identify proteins or genes that contain a well-defined sequence pattern. Many databases do not provide straightforward or readily available query tools to perform simple searches, such as identifying transcription binding sites, protein motifs, or repetitive DNA sequences. However, in many cases simple pattern-matching searches can reveal a wealth of information. We present in this paper a regular expression pattern-matching tool that was used to identify short repetitive DNA sequences in human coding regions for the purpose of identifying potential mutation sites in mismatch repair deficient cells.ResultsKangaroo is a web-based regular expression pattern-matching program that can search for patterns in DNA, protein, or coding region sequences in ten different organisms. The program is implemented to facilitate a wide range of queries with no restriction on the length or complexity of the query expression. The program is accessible on the web at http://bioinfo.mshri.on.ca/kangaroo/ and the source code is freely distributed at http://sourceforge.net/projects/slritools/.ConclusionA low-level simple pattern-matching application can prove to be a useful tool in many research settings. For example, Kangaroo was used to identify potential genetic targets in a human colorectal cancer variant that is characterized by a high frequency of mutations in coding regions containing mononucleotide repeats.

MacMatch: a tool for pattern-based protein secondary structure prediction.

A program is described for predicting the secondary structure of globular proteins on an Apple Macintosh computer. MacMatch produces turn predictions using augmented regular expression pattern matching. Helix and strand predictions are based on a neural network. Protein structural class (alpha/alpha), beta/beta, alpha/beta can be exploited to improve secondary structure prediction. The program is simple to use, and the package includes sets of tested patterns and trained neural networks.

A Four Russians algorithm for regular expression pattern matching

Given a regular expression R of length P and a word A of length N , the membership problem is to determine if A is in the language denoted by R . An O ( PN /lg N ) time algorithm is presented that is based on a lg N speedup of the standard O ( PN ) time simulation of R 's nonderministic finite automaton on A using a combination of the node-listing and “Four-Russians” paradigms. This result places a new worst-case upper bound on regular expression pattern matching. Moreover, in practice the method provides an implementation that is faster than existing software for small regular expressions.