Extended parameterized Burrows–Wheeler transform

IF 3.4 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2025-09-11 DOI:10.1016/j.is.2025.102611

Eric M. Osterkamp , Dominik Köppl

引用次数: 0

Abstract

The Burrows–Wheeler transform (BWT) lies at the heart of succinct and compressed full-text indexes for pattern matching queries. Notable variants are (a) the extended BWT (eBWT) capable to index multiple circular texts for pattern matching, or (b) the parameterized BWT (pBWT) for parameterized pattern matching. A natural extension is the combination of the virtues of both variants into a new data structure, whose name we coin with extended parameterized BWT (epBWT). We show that the epBWT supports pattern matching in context of parameterized pattern matching on multiple circular texts, within the same complexities as known solutions presented for the pBWT [Kim and Cho, IPL’21] for patterns not longer than the shortest indexed text. Additionally, we show how to compute the epBWT within the same complexities as [Iseri et al., ICALP’24], i.e., in compact space and quasilinear time. As an application, we extend the matching statistics problem to the parameterized pattern matching setting on circular texts.

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扩展参数化Burrows-Wheeler变换

Burrows-Wheeler变换（BWT）是用于模式匹配查询的简洁压缩全文索引的核心。值得注意的变体是(a)能够索引多个循环文本以进行模式匹配的扩展BWT (eBWT)，或(b)用于参数化模式匹配的参数化BWT （pBWT）。自然扩展是将这两种变体的优点结合到一个新的数据结构中，我们将其名称与扩展参数化BWT （epBWT）一起命名。我们证明了epBWT在多个圆形文本的参数化模式匹配背景下支持模式匹配，其复杂性与pBWT提出的解决方案相同[Kim和Cho， IPL ' 21]，适用于不超过最短索引文本的模式。此外，我们展示了如何在与[Iseri等人，ICALP ' 24]相同的复杂性下计算epBWT，即在紧空间和拟线性时间内。作为应用，我们将匹配统计问题扩展到圆形文本的参数化模式匹配设置。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.