ENHANCING SEARCH PROCESSES: EVOLUTION OF PATTERN SEARCHING ALGORITHMS

Abstract

Pattern searching is a fundamental problem in computer science with diverse applications ranging from text editing to bioinformatics and computer vision. This paper explores the significance of pattern searching algorithms in various domains and discusses the challenges associated with their implementation. A pattern represents a non-empty language and can be described by a string or a set of strings. String matching, the core task of pattern searching, involves finding occurrences of a given pattern within a larger string, irrespective of the alphabetic order. The problem of pattern searching arises in database searches, substring pattern matching, and numerous other applications that require efficient search processes. This study emphasizes the need for fast and efficient pattern searching algorithms, considering the exponential growth in data availability. Different algorithms possess distinct advantages and disadvantages, necessitating careful selection based on the specific application requirements. Furthermore, the paper highlights the extensive applications of pattern searching, including DNA and protein sequence analysis, spell checking, computer viruses’ detection, signature matching, and language translation. Real-time detection of face masks and World Wide Web search engines are other domains where pattern searching finds utility. While several algorithms exist, their scalability and computational costs pose challenges for large databases and DNA sequences. Therefore, it is crucial to develop algorithms that overcome these limitations and improve performance. The paper discusses the need for advancements in pattern searching algorithms to address the evolving demands of data processing and storage.