arXiv:2604.13037cs.DBcs.AI2026-04

在线工具OVT-MLCS可高效挖掘长序列中的多重最长公共子序列

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

论文配图:OVT-MLCS: An Online Visual Tool for MLCS Mining from Long or Big Sequences
图 1 · 摘自论文原文
  • 基于关键点的新算法KP-MLCS,支持大序列的精确挖掘
  • 能处理长度达10,000以上的序列,支持图形化实时展示
  • 适合生物信息、文本分析等需要长序列比对的研究者使用

从三个或更多序列中挖掘多重最长公共子序列(MLCS)是有限字母表Σ上的经典NP难问题,在多个应用领域具有重要意义。然而,目前尚无精确的MLCS算法或工具能有效处理长度≥1,000或规模≥10,000的序列,严重制约了大规模长序列在各领域的应用与发展。为此,本文提出一种基于关键点的新型MLCS算法KP-MLCS,并设计一种紧凑表示所有挖掘出的MLCS并快速揭示其共性模式的新方法。此外,通过引入实时图形可视化与序列化技术,开发出在线可视化MLCS挖掘工具OVT-MLCS。该工具可实现对长度3至5000的长或大序列中MLCS的有效在线挖掘、存储、下载,以图形和文本形式呈现结果,并提供友好的交互功能,便于用户检查与分析。我们相信,OVT-MLCS的功能将推动MLCS在更广泛场景下的应用。

原文摘要 · Abstract (English)

Mining multiple longest common subsequences (\textit{MLCS}) from a set of sequences of three or more over a finite alphabet $Σ$ (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}.

序列挖掘可视化算法工具

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