PG-HIVE: 属性图的混合增量模式发现
PG-HIVE: Hybrid Incremental Schema Discovery for Property Graphs
AI总结：
PG-HIVE 通过混合增量模式发现方法，实现属性图的自动模式发现，提升查询效率和准确性。
摘要：
属性图已经迅速成为表示和管理复杂互联数据的事实标准， powering 跨领域的应用，从知识图谱到社交网络。尽管有优势，其无模式的性质对集成、探索、可视化和高效查询提出了重大挑战。为解决这一差距，我们提出了 PG-HIVE，一种用于属性图自动模式发现的新框架。PG-HIVE 超越现有方法，通过揭示隐含的节点和边类型，推断属性数据类型、约束和基数，即使在没有显式标记信息的情况下也能做到这一点。利用一种独特的 Locality-Sensitive Hashing 与基于属性和标签的聚类相结合的方法，PG-HIVE 在大规模上识别结构相似性。此外，它引入了增量模式发现，消除新数据到来时的高成本重新计算。通过广泛的实验，我们证明 PG-HIVE 在准确性和效率上都优于最先进的解决方案，准确率提高高达 65%（节点）和 40%（边），效率提高高达 1.95 倍，解锁了模式感知属性图管理的全部潜力。
英文摘要：
Property graphs have rapidly become the de facto standard for representing and managing complex, interconnected data, powering applications across domains from knowledge graphs to social networks. Despite the advantages, their schema-free nature poses major challenges for integration, exploration, visualization, and efficient querying. To bridge this gap, we present PG-HIVE, a novel framework for automatic schema discovery in property graphs. PG-HIVE goes beyond existing approaches by uncovering latent node and edge types, inferring property datatypes, constraints, and cardinalities, and doing so even in the absence of explicit labeling information. Leveraging a unique combination of Locality-Sensitive Hashing with property- and label-based clustering, PG-HIVE identifies structural similarities at scale. Moreover, it introduces incremental schema discovery, eliminating costly recomputation as new data arrives. Through extensive experimentation, we demonstrate that PG-HIVE consistently outperforms state-of-the-art solutions, in both accuracy (by up to 65% for nodes and 40% for edges), and efficiency (up to 1.95x faster execution), unlocking the full potential of schema-aware property graph management.
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