Application Of Exact String Matching Algorithms Towards SMILES Representation Of Chemical Structure.

Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information

Performance Study of the Running Times of well known Pattern Matching Algorithms for Signature-based Intrusion Detection Systems

Intrusion detection system (IDS) is the basic component of any network defense scheme. Signature based intrusion detection techniques are widely used in networks for fast response to detect threats. One of the main challenges faced by signature-based IDS is that every signature requires an entry in the database, and so a complete database might contain hundreds or even thousands of entries. Each packet is to be compared with all the entries in the database. This can be highly resource-consuming and doing so will slow down the throughput and making the IDS vulnerable. Since pattern matching computations dominate in the overall performance of a Signature-based IDS, efficient pattern matching algorithms should be used which use minimal computer storage and which minimize the searching response time. In this paper we present a performance study of the running times of different well known pattern matching algorithms using multiple sliding windows approach. DOI: 10.17762/ijritcc2321-8169.150613

Exact string matching algorithms for searching DNA and protein sequences and searching chemical databases

The enormous quantities of biological and chemical files and databases are likely to grow year on year, consequently giving rise to the need to develop string-matching algorithms capable of minimizing the searching response time. Being aware of this need, this thesis aims to develop string matching algorithms to search biological sequences and chemical structures by studying exact string matching algorithms in detail. As a result, this research developed a new classification of string matching algorithms containing eight categories according to the pre-processing function of algorithms and proposed five new string matching algorithms; BRBMH, BRQS, Odd and Even algorithm (OE), Random String Matching algorithm (RSMA) and Skip Shift New algorithm (SSN). The main purpose behind the proposed algorithms is to reduce the searching response time and the total number of comparisons. They are tested by comparing them with four well- known standard algorithms, Boyer Moore Horspool (BMH), Quick Search (QS), TVSBS and BRFS. This research applied all of the algorithms to sample data files by implementing three types of tests. The number of comparison tests showed a substantial difference in the number of comparisons our algorithms use compared to the non-hybrid algorithms such as QS and BMH. In addition, the tests showed considerable difference between our algorithms and other hybrid algorithm such as TVSBS and BRFS. For instance, the average elapsed search time tests showed that our algorithms presented better average elapsed search time than the BRFS, TVSBS, QS and BMH algorithms, while the average number of tests showed better number of attempts compared to BMH, QS, TVSBS and BRFS algorithms. A new contribution has been added by this research by using the fastest proposed algorithm, the SSN algorithm, to develop a chemical structure searching toolkit to search chemical structures in our local database. The new algorithms were paralleled using OpenMP and MPI parallel models and tested at the University of Science Malaysia (USM) on a Stealth Cluster with different number of threads and processors to improve the speed of searching pattern in the given text which, as we believe, is another contribution

Complexity of Sequential Pattern Matching Algorithms

. We formally define a class of sequential pattern matching algorithms that includes all variations of Morris-Pratt algorithm. For the last twenty years it was known that the complexity of such algorithms is bounded by a linear function of the text length. Recently, substantial progress has been made in identifying lower bounds. We now prove there exists asymptotically a linearity constant for the worst and the average cases. We use Subadditive Ergodic Theorem and prove an almost sure convergence. Our results hold for any given pattern and text and for stationary ergodic pattern and text. In the course of the proof, we establish some structural property, namely, the existence of "unavoidable positions " where the algorithm must stop to compare. This property seems to be uniquely reserved for Morris-Pratt type algorithms (e.g., Boyer and Moore algorithm does not possess this property). 1 Introduction The complexity of string searching algorithms has been discussed in various papers (cf..

Complexity of sequential pattern matching algorithms

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1995 n.2549 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Complexity of Sequential Pattern Matching Algorithms

We formally define a class of sequential pattern matching algorithms that includes all variations of Morris-Pratt algorithm. For the last twenty years it was known that the complexity of such algorithms is bounded by a linear function of the text length. Recently, substantial progress has been made in identifying lower bounds. We now prove there exists asymptotically a linearity constant for the worst and the average cases. We use Subadditive Ergodic Theorem and prove an almost sure convergence. Our results hold for any given pattern and text and for stationary ergodic pattern and text. In the course of the proof, we establish some structural property, namely, the existence of "unavoidable positions" where the algorithm must stop to compare. This property seems to be uniquely reserved for Morris-Pratt type algorithms since we observe that a popular pattern matching algorithm proposed by Boyer and Moore does not possess this property. Keywords: String searching, pattern matching, analys..