BL: 大型图上可达性查询的高效索引

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Changyong Yu;Tianmei Ren;Wenyu Li;Huimin Liu;Haitao Ma;Yuhai Zhao
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引用次数: 0

摘要

可达性查询在社交网络、语义网和生物信息网络等许多领域都有重要应用。如何提高有向无环图(DAG)的查询效率一直是可达性查询研究的主要问题。现有的方法要么无法充分剪切不可达对,要么在索引大小和查询时间上都表现不佳。本文提出了用于大型图中可达性查询的通用索引框架 BL(Bridging Label)。首先,我们总结了 BL 与现有标签索引之间的关系。其次,我们提出了一种名为 minBL 的特定索引,它可以避免冗余标签。此外,我们还提出了 TFD-minBL 和 CTFD-minBL,它们分别在基于 TFD 的置换单程和增量下生成 minBL。最后,我们在真实和合成数据集上进行了大量广泛的实验。实验结果表明,与现有的可达性查询方法相比,我们的方法速度更快,使用的存储开销更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BL: An Efficient Index for Reachability Queries on Large Graphs
Reachability query has important applications in many fields such as social networks, Semantic Web, and biological information networks. How to improve the query efficiency in directed acyclic graph ( DAG ) has always been the main problem of reachability query research. Existing methods either can't prune unreachable pairs enough or can't perform well on both index size and query time. In this paper, we propose BL ( Bridging Label ), a general index framework for reachability queries in large graphs. First, we summarize the relationships between BL and existing label indices. Second, we propose a kind of specific index, named minBL, which can avoid redundant labels. Moreover, we propose TFD-minBL and CTFD-minBL, which generate minBL under the TFD-based permutation single-pass and in incremental, respectively. Finally, we conduct a large number of extensive experiments on real and synthetic datasets. The experimental results show that our methods are much faster and use less storage overhead than the existing reachability query methods.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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