在大型图上回答资源受限的最短路径查询

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haoran Qian;Weiguo Zheng;Zhijie Zhang;Bo Fu
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引用次数: 0

摘要

在基于图的应用程序中,约束最短路径问题是一个基本且具有挑战性的任务。本文形式化并研究了$Min$-$Max$资源约束最短路径($Min$-$Max$ RCSP)问题,它推广了已有的$Max$ RCSP问题。目标是在两个查询节点之间找到一条成本最小的简单路径,同时受最小和最大限制之间的资源约束。该问题在延迟网络和交通等领域有着广泛的应用。然而,我们从理论上证明了计算最优解是np困难的。我们提出了一种两阶段的方法,包括基于资源的图缩减,然后是成本导向的路径生成。为了减少昂贵的非循环检查成本,我们引入了基于最短路径树的祖先检查技术。此外,我们提出了一种更快的增量搜索方法,该方法考虑了路径成本和资源约束,同时避免了非循环检查。在20个真实图上进行的大量实验一致地证明了我们提出的方法的优越性,在产生高质量解决方案的同时,在时间效率上比基线算法提高了两个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Answering Min-Max Resource-Constrained Shortest Path Queries Over Large Graphs
The constrained shortest path problem is a fundamental and challenging task in applications built on graphs. In this paper, we formalize and study the $Min$ - $Max$ resource-constrained shortest path ( $Min$ - $Max$ RCSP) problem, which generalizes the well-studied $Max$ RCSP problem. The objective is to find a simple path of minimum cost between two query nodes, subject to resource constraints between minimum and maximum limits. This problem has wide applications in fields such as delay networks and transportation. However, we theoretically prove that computing the optimal solution is NP-hard. We propose a two-stage approach that involves resource-based graph reduction followed by cost-guided path generation. To reduce the cost of expensive acyclicity checking, we introduce the technique of ancestor checking based on the shortest path tree. Furthermore, we present an even faster incremental search approach that considers both the path cost and resource constraints while avoiding acyclicity checking. Extensive experiments on twenty real graphs consistently demonstrate the superiority of our proposed methods, achieving up to two orders of magnitude improvement in time efficiency over the baseline algorithms while producing high-quality solutions.
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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