A New String Matching Algorithm for Analyzing University Curriculum with Respect to Current Job Circular

Abstract

Mining data from text is often becomes a crucial part of data mining tasks. With the growing tendency of using cloud and sharing more and more les over the internet, the necessity of applying a string matching algorithm in text mining has increased rapidly in present time. In recent years many pattern matching algorithms are proposed to enhance information retrieval from large le(s), especially in search engines as a mean of searching a certain term throughout multiple web pages to rank pages. These tasks require a faster string matching that can nd a certain pattern from a text with a very minimal waste of time. This can be ensured by using an algorithm that makes less character comparisons and pattern shifts while searching. In this paper, we're proposing a new algorithm named Back and Forth Matching (BFM) algorithm to perform string matching tasks in faster way by matching a pattern from both the forward and backward direction. A comparison of this algorithm with other algorithm shows a tremendous improvement in matching strings in large text les. For this advantage, we have implemented this algorithm in searching through universities course curriculum in Bangladesh context and compare it with the existing job circulars, in order to nd out how much the university curriculum is relevant with respect to current job markets. This will provide universities to learn about their laggings and thereby make necessary improvements as per the suggestions generated from our project.