OVT-MLCS: An Online Visual Tool for MLCS Mining from Long or Big Sequences

arXiv cs.AI / 4/16/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical UsageModels & Research

Key Points

  • The paper introduces OVT-MLCS, an online visual tool designed to mine multiple longest common subsequences (MLCS) from long (length ≥ 1,000) and big (length ≥ 10,000) sequences where exact MLCS tools have struggled due to NP-hardness.
  • It proposes a key-point-based MLCS algorithm (KP-MLCS) and a compact representation method for all mined MLCSs to quickly reveal common patterns.
  • OVT-MLCS includes real-time graphical visualization plus serialization to support interactive inspection, along with storing and downloading results as graphs and text.
  • The tool is demonstrated to handle datasets with 3 to 5,000 sequences, aiming to expand practical MLCS usage across application domains.

Abstract

Mining multiple longest common subsequences (\textit{MLCS}) from a set of sequences of three or more over a finite alphabet \Sigma (a classical NP-hard problem) is an important task in a wide variety of application fields. Unfortunately, there is still no exact \textit{MLCS} algorithm/tool that can handle long (length \ge 1,000) or big (length \ge 10,000) sequences, which seriously hinders the development and utilization of massive long or big sequences from various application fields today. To address the challenge, we first propose a novel key point-based \textit{MLCS} algorithm for mining big sequences, called \textit{KP-MLCS}, and then present a new method, which can compactly represent all mined \textit{MLCSs} and quickly reveal common patterns among them. Furthermore, by introducing some new techniques, e.g., real-time graphic visualization and serialization, we have developed a new online visual \textit{MLCS} mining tool, called OVT-MLCS. OVT-MLCS demonstrates that it not only enables effective online mining, storing, and downloading of \textit{MLCSs} in the form of graphs and text from long or big sequences with a scale of 3 to 5000 but also provides user-friendly interactive functions to facilitate inspection and analysis of the mined \textit{MLCS}s. We believe that the functions provided by OVT-MLCS will promote stronger and wider applications of \textit{MLCS}.