What question did this study set out to answer?

The aim is to consolidate and analyze various pattern-matching techniques across different categories.

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February 10, 2026Open Access

A Comprehensive Meta-Analysis of Pattern-Matching Techniques: Exact, Approximate, Semantic, and Multi-Dimensional Approaches

Key Points

The aim is to consolidate and analyze various pattern-matching techniques across different categories.
Conducted a meta-analysis of existing pattern-matching techniques.
Categorized techniques by operational characteristics and computational complexity.
Reviewed applications across multiple domains such as text processing, bioinformatics, and computer vision.
Identified strengths and limitations of exact, approximate, semantic, and multi-dimensional approaches.
Highlighted gaps in the current literature.
Outlined open challenges for future research in the field.

Abstract

Pattern matching is a foundational operation in computer science with applications spanning text processing, bioinformatics, computer vision, and data mining. Numerous techniques exist, ranging from exact and approximate string matching to semantic and multi-dimensional approaches. Despite extensive research in individual categories, there exists no single comprehensive survey that consolidates all pattern-matching paradigms. This paper presents a meta-analysis of pattern- matching techniques, categorizing them by operational char- acteristics, computational complexity, and application domains. We provide a comparative discussion of their strengths and limitations and identify open challenges that warrant further research

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