整合文档、关系与图模型，实现高效探索式图分析。
Siren Federate: Bridging document, relational, and graph models for exploratory graph analysis
- 融合三种数据模型，统一支持复杂查询。
- 路径查询中中间结果增长速度显著降低。
- 适合需要实时分析大规模异构知识图谱的用户。
探索性分析工作流需要在大型异构知识图谱上进行交互式分析，现有数据库难以满足此类任务需求。本文提出Siren Federate系统架构，通过融合文档型、关系型和图模型，高效支持探索式图分析。技术贡献包括分布式连接算法、自适应查询规划、查询计划折叠、语义缓存以及路径查询的半连接分解。其中，半连接分解有效缓解了路径查询中中间结果指数级增长的问题。实验表明，Siren Federate具备低延迟特性，且在数据量、用户数和计算节点数量增加时仍具有良好扩展性。
原文摘要 · Abstract (English)
Investigative workflows require interactive exploratory analysis on large heterogeneous knowledge graphs. Current databases show limitations in enabling such task. This paper discusses the architecture of Siren Federate, a system that efficiently supports exploratory graph analysis by bridging document-oriented, relational and graph models. Technical contributions include distributed join algorithms, adaptive query planning, query plan folding, semantic caching, and semi-join decomposition for path query. Semi-join decomposition addresses the exponential growth of intermediate results in path-based queries. Experiments show that Siren Federate exhibits low latency and scales well with the amount of data, the number of users, and the number of computing nodes.
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