子串压缩变化和lz78派生

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2025-04-25 DOI:10.1016/j.is.2025.102553

Dominik Köppl

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引用次数: 0

摘要

我们提出了半贪婪Lempel-Ziv 78 （LZ78）、Lempel-Ziv Double （LZD）和线性时间整数字母的Lempel-Ziv - miller - wegman （LZMW）分解算法。对于LZD和LZMW，我们还提出了可以在线性时间内构建的数据结构，这可以解决这些分解的子串压缩问题，这些分解在输出大小上是线性的。对于子字符串压缩，我们给出了lexparse和闭分解的第一个结果。

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Substring compression variations and LZ78-Derivates

We propose algorithms computing the semi-greedy Lempel–Ziv 78 (LZ78), the Lempel–Ziv Double (LZD), and the Lempel–Ziv–Miller–Wegman (LZMW) factorizations in linear time for integer alphabets. For LZD and LZMW, we additionally propose data structures that can be constructed in linear time, which can solve the substring compression problems for these factorizations in time linear in the output size. For substring compression, we give the first results for lexparse and closed factorizations.

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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.