A Compact DAG for Storing and Searching Maximal Common Subsequences

TL;DR

This paper introduces a compact directed acyclic graph (DAG) structure that efficiently stores and searches all Maximal Common Subsequences (MCSs) between two strings, enabling enumeration, counting, and random access in polynomial time.

Contribution

It presents the first polynomial-sized DAG for MCSs, enabling efficient operations and closing the complexity gap with Longest Common Subsequences (LCSs).

Findings

Constructed a polynomial-size DAG for MCSs

Enabled enumeration in Constant Amortized Time (CAT)

Supported efficient counting and random access operations

Abstract

Maximal Common Subsequences (MCSs) between two strings X and Y are subsequences of both X and Y that are maximal under inclusion. MCSs relax and generalize the well known and widely used concept of Longest Common Subsequences (LCSs), which can be seen as MCSs of maximum length. While the number both LCSs and MCSs can be exponential in the length of the strings, LCSs have been long exploited for string and text analysis, as simple compact representations of all LCSs between two strings, built via dynamic programming or automata, have been known since the '70s. MCSs appear to have a more challenging structure: even listing them efficiently was an open problem open until recently, thus narrowing the complexity difference between the two problems, but the gap remained significant. In this paper we close the complexity gap: we show how to build DAG of polynomial size-in polynomial time-which allows for efficient operations on the set of all MCSs such as enumeration in Constant Amortized Time per solution (CAT), counting, and random access to the i-th element (i.e., rank and select operations). Other than improving known algorithmic results, this work paves the way for new sequence analysis methods based on MCSs.