Lynx: A Graph Query Framework for Multiple Heterogeneous Data Sources

IF 2.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611587
Zhihong Shen, Chuan Hu, Zihao Zhao
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Abstract

Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query framework, to simplify querying graph data by converting complex statements into basic graph operations. Instead of connecting directly to the data sources, Lynx retrieves data through user-implemented interfaces for those graph operations. We demonstrate Lynx's capabilities through real-world scenarios, showcasing Lynx's ability to process graph queries on multiple heterogeneous data sources and also to be used as a generic graph query engine development framework.
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Lynx:面向多个异构数据源的图形查询框架
图模型由于能够对实体之间的复杂关系进行建模,在现代应用程序中越来越受欢迎。用户倾向于通过图形操作(例如,图形导航和探索)将数据作为图形来查询。然而,很大一部分数据驻留在关系数据库或其他存储系统中。以图的形式统一查询多个异构数据源会带来挑战。传统的解决方案受到耗时的数据集成、昂贵的开发工作和不完整的查询需求的限制。因此,我们开发了通用图查询框架Lynx,通过将复杂语句转换为基本图操作来简化图数据的查询。Lynx没有直接连接到数据源,而是通过用户实现的接口为那些图操作检索数据。我们通过实际场景演示Lynx的功能,展示Lynx在多个异构数据源上处理图形查询的能力,以及作为通用图形查询引擎开发框架使用的能力。
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来源期刊
Proceedings of the Vldb Endowment
Proceedings of the Vldb Endowment Computer Science-General Computer Science
CiteScore
7.70
自引率
0.00%
发文量
95
期刊介绍: The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
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