In this research, we propose a fast pattern matching algorithm: The Two Sliding Windows (TSW) algorithm. The algorithm makes use of two sliding windows, each window has a size that is equal to the pattern length. Both windows slide in parallel over the text until the first occurrence of the pattern is found or until both windows reach the middle of the text. The experimental results show that TSW algorithm is superior to other algorithms especially when the pattern occurs at the end of the text

Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network security and pattern recognition. This paper has presented a new pattern matching algorithm-Enhanced ERS-A, which is an improvement over ERS-S algorithm. In ERS-A, two sliding windows are used to scan the text from the left and the right simultaneously. The proposed algorithm also scans the text from the left and the right simultaneously as well as making comparisons with the pattern from both sides simultaneously. The comparisons done between the text and the pattern are done from both sides in parallel. The shift technique used in the Enhanced ERS-A is the four consecutive characters in the text immediately following the pattern window. The experimental results show that the Enhanced ERS-A has enhanced the process of pattern matching by reducing the number of comparisons performed.

This paper presents an efficient pattern matching algorithm (FSW). FSW improves the searching process for a pattern in a text. It scans the text with the help of four sliding windows. The windows are equal to the length of the pattern, allowing multiple alignments in the searching process. The text is divided into two parts; each part is scanned from both sides simultaneously using two sliding windows. The four windows slide in parallel in both parts of the text. The comparisons done between the text and the pattern are done from both of the pattern sides in parallel. The conducted experiments show that FSW achieves the best overall results in the number of attempts and the number of character comparisons compared to the pattern matching algorithms: Two Sliding Windows (TSW), Enhanced Two Sliding Windows algorithm (ETSW) and Berry-Ravindran

The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.

A mobile host has small memory, relatively slow processor, low power batteries, and communicate over low bandwidth wireless communication links. Existing mutual exclusion algorithms for distributed systems are not enough for mobile systems because of several limitations. In this study, a mutual exclusion algorithm that is more suitable for mobile computer systems is developed. The algorithm tends to minimize the number of messages needed to be transmitted in the system, by reducing the number of sites involved in the mutual exclusion decision, and reducing the amount of storage needed at different sites of the system