摘要
随着信息技术的迅猛发展，图数据库在社交网络分析、生物信息学等领域展现出独特优势，但其查询效率问题亟待解决。为此，本研究聚焦于图数据库查询语言与算法优化，旨在提升查询性能并增强表达能力。通过深入剖析现有图查询语言的语义结构和执行机制，提出一种融合模式匹配与路径搜索的新查询语言框架，该框架能够更灵活地描述复杂查询需求。同时，针对传统遍历算法存在的冗余计算问题，设计了基于节点重要性评估的智能剪枝策略，有效减少了不必要的计算量。实验结果表明，在多个真实数据集上，新方法相较于传统方案平均查询响应时间缩短了35%，且在大规模稀疏图中表现尤为突出。此外，本研究还引入了增量式查询处理机制，使得在动态更新场景下能够快速响应变化，保持查询结果的实时性。这一创新不仅提高了图数据库系统的整体性能，也为相关应用领域提供了更为高效可靠的解决方案，为推动图数据库技术的发展做出了积极贡献。
关键词：图数据库查询优化；模式匹配与路径搜索；节点重要性评估；增量式查询处理；查询性能提升
Abstract
With the rapid advancement of information technology, graph databases have demonstrated unique advantages in fields such as social network analysis and bioinformatics, yet their query efficiency remains a critical issue to be addressed. This study focuses on the optimization of graph database query languages and algorithms to enhance query performance and expressive power. By thoroughly analyzing the semantic structures and execution mechanisms of existing graph query languages, we propose a novel query language fr amework that integrates pattern matching with path searching, enabling more flexible desc riptions of complex query requirements. Additionally, to address the issue of redundant computations in traditional traversal algorithms, we design an intelligent pruning strategy based on node importance evaluation, effectively reducing unnecessary computational overhead. Experimental results show that, across multiple real-world datasets, the new approach achieves an average reduction of 35% in query response time compared to conventional methods, with particularly notable performance improvements in large-scale sparse graphs. Furthermore, this research introduces an incremental query processing mechanism, allowing for rapid responses to changes in dynamic update scenarios and maintaining the real-time accuracy of query results. This innovation not only improves the overall performance of graph database systems but also provides more efficient and reliable solutions for relevant application domains, contributing positively to the development of graph database technology.
Keywords：Graph Database Query Optimization; Pattern Matching And Path Search; Node Importance Evaluation; Incremental Query Processing; Query Performance Improvement
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
二、图数据库查询语言的优化 2
(一) 查询语言语法结构分析 2
(二) 查询表达式的优化策略 2
(三) 查询执行计划的改进方法 3
三、图算法性能优化研究 3
(一) 常用图算法效率分析 3
(二) 算法并行化技术探讨 4
(三) 算法存储结构优化 5
四、实验评估与应用案例 5
(一) 实验环境与数据集构建 5
(二) 查询优化效果评估 6
(三) 应用场景实例分析 7
结 论 9
参考文献 10
