TRGST:路径拓扑关系的增强型广义后缀树

IF 3 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Carlos Quijada-Fuentes , M. Andrea Rodríguez , Diego Seco
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引用次数: 0

摘要

本文介绍 TRGST 数据结构,该结构旨在处理与网络中以站点序列表示的路径之间的拓扑关系有关的查询。举例来说,这些路径可能对应于公共交通网络中的站点,我们感兴趣的查询是检索至少有 k 个连续站点的路径。虽然空间对象之间的拓扑关系已受到广泛关注,但在轨迹路径中如何高效处理这些关系,同时考虑时间和空间效率,仍是一个探索相对较少的领域。受模式匹配实现的启发,TRGST 数据结构是在广义后缀树的基础上构建的。其目的是提供一组路径的紧凑表示,并利用该结构固有的模式搜索功能高效处理拓扑关系查询。本文详细介绍了 TRGST 的结构和算法,随后利用真实数据和合成数据进行了性能分析。结果表明,TRGST 在查询时间和空间利用率方面都具有显著的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRGST: An enhanced generalized suffix tree for topological relations between paths

This paper introduces the TRGST data structure, which is designed to handle queries related to topological relations between paths represented as sequences of stops in a network. As an example, these paths could correspond to stops on a public transport network, and a query of interest is to retrieve paths that share at least k consecutive stops. While topological relations among spatial objects have received extensive attention, the efficient processing of these relations in the context of trajectory paths, considering both time and space efficiency, remains a relatively less explored domain. Taking inspiration from pattern matching implementations, the TRGST data structure is constructed on the foundation of the Generalized Suffix Tree. Its purpose is to provide a compact representation of a set of paths and to efficiently handle topological relation queries by leveraging the pattern search capabilities inherent in this structure. The paper provides a detailed account of the structure and algorithms of TRGST, followed by a performance analysis utilizing both real and synthetic data. The results underscore the remarkable scalability of the TRGST in terms of both query time and space utilization.

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来源期刊
Information Systems
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.
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