Corasick is a standard string matching algorithm. It can match multiple patterns simultaneously and affirmed deterministic performance under all circumstances. Aho- Corasick feed solutions to various real world applications like intrusion detection systems, text mining, search engine, multimedia and computational biology. In order to improve performance of these applications parallelization of Aho- Corasick is crucial. PFAC (Parallel Failure Less Aho- Corasick) algorithm provides high degree of parallelization in Aho-Corasick algorithm. PFAC implementation on GPGPU's architecture may consist various implementation issues. In this paper discrete implementation issues of PFAC on GPGPU's using OpenCL, their solutions and comparative analysis are discussed. KeywordsAC Techniques, PFAC Techniques, Parallel AC

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PFAC Implementation Issues and their Solutions on GPGPU's using OpenCL

The enormous amount of data has been boundlessly growing over the last few decades and expected to exponentially do so in the future. However, a substantial size of this accumulated amount is discarded anyhow. The processing capabilities have been considered as one of the major barriers in the way of exploiting this priceless mine. Therefore, the issue has absorbed considerable part of researchers' concentration. OLAP has been considered as a powerful method for analysing excessive size of data that works closely to intelligent business, medical fields. Yet, such a method will always need increasing processing resources. Numerous enhancements have been suggested in order to improve OLAP performance, part of them has gone to the processing capabilities whereby parallel processing has occupied a sizeable space. A heterogeneous computing is a relatively recent approach that is being under examination. In this paper, through experimental results and based on Rabin-Karp Algorithm; we propose an optimized heterogeneous solution that takes into account the benefits and the boundaries in order to achieve a better OLAP performance in terms of response time with three times gain. In the light of our results; we present the achieved gain and possible future trends.

Optimizing OLAP heterogeneous computing based on Rabin-Karp Algorithm

With the rapid growth of Internet, E-mail, with its convenient and efficient characteristics, has become an important means of communication in people‟s life. It reduces the cost of communication. It comes with Spam. Spam emails, also known as „junk e-mails‟, are unsolicited one‟s sent in bulk with hidden or forged identity of the sender, address, and header information. It is vital to pursue more effective spam filtering approaches to maintain normal operations of e-mail systems and to protect the interests of email users. In this paper we developed a Spam filter based on Bayesian filtering method using Aho-corasick and PFAC string matching algorithm. This filter developed an improved version of spam filter based on traditional Bayesian spam filtering to improve spam filtering efficiency, and to reduce chances of misjudgement of malignant spam. For further improvement of Spam filtering process we are transform the filter in to parallel spam filter on GPGPU's by using PFAC Algorithm.

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Serial and Parallel Bayesian Spam Filtering using Aho-Corasick and PFAC

The Analysis of KMP Algorithm and its Optimization

String matching is an integral part of various important areas of computer science viz search engines, DNA matching, information retrieval, security, and biological systems. String matching algorithms are used in finding a pattern in a string. In this paper, the authors have given a novel algorithm to reduce the time complexity of string matching. The proposed algorithm is based on the concept of hashing with chaining. Further, the authors have found reduced time complexity in most of the cases and equal in few.

Hybrid Approach to Reduce Time Complexity of String Matching Algorithm Using Hashing with Chaining

String matching is a special kind of pattern recognition problem, which finds all occurrences of a given pattern string in a given text string. The technology of two-dimensional string matching is applied broadly in many information processing domains. A good two-dimensional string matching algorithm can effectively enhance the searching speed. In this paper, the KMP algorithm, Rabin-Karp algorithm and their combinatorial are presented and compared, by a number of tests at diverse data scales, to validate the efficiency of these three algorithms.

Hui Wang

Papers

Comparison of Two-Dimensional String Matching Algorithms