Efficient Regex Matching with Sparse Counting-Sets

2026-06-25

AI Summary

This work addresses the significant performance overhead incurred by conventional regular expression matching algorithms when handling counting operators, primarily due to frequent branch-induced copying of entire counter sets. To mitigate this bottleneck, the paper proposes a sparse counter set representation that maintains only essential counter values, coupled with an efficient update strategy that drastically reduces the number of copy operations during matching. By minimizing redundant data replication in branching scenarios, the proposed approach substantially improves the matching efficiency of regular expressions involving counting constructs, effectively alleviating a key performance limitation in existing methods.

Abstract

Regular expressions with counting operations (c-regexes) offer a compact representation of repeating patterns by allowing numerical bounds to be added to subexpressions. Recent work introduced the counting-set data structure, which allows simultaneous updates of multiple counter values for efficient matching. However, this approach suffers from a performance bottleneck when counting-sets must be replicated due to the presence of branching transitions. We propose a sparse counting-set approach, which reduces the replication overhead by maintaining only essential counter values, thereby yielding a more efficient matching algorithm.

Problem

• regex matching
• counting operations
• counting-set
• branching transitions
• replication overhead

Innovation

• sparse counting-set
• regular expression matching
• counting operations
• replication overhead
• efficient matching