AI 中文总结
该研究针对流图查询场景，提出TrieGS共享内存引擎，实现快照一致的常延迟枚举，兼顾更新吞吐量与查询结果的一致性，解决了现有方法的相关缺陷。
AI 中文摘要
连续图查询引擎必须处理边更新，同时向并发消费者提供当前查询结果。现有动态常延迟枚举方法提供了强大的每回答延迟保证，但通常被表述为维护后枚举的过程；相反，多核图流引擎强调更新吞吐量和匹配发现，却未为并发全结果枚举提供查询级快照保证。我们提出TrieGS，这是一种在流图上维护查询特定CDE状态的共享内存引擎。逻辑上，TrieGS采用经典的自由连接见证子树枚举器；动态上，它使用精确带符号增量维护多重性负载；物理上，RDF术语被字典编码，所需访问结构被实现为版本化LFNT关系（无锁嵌套Trie）。新的系统问题并非仅关系级快照：枚举任务必须在所有相互依赖的基础关系、投影、视图和索引上观察到一个一致状态。因此，TrieGS仅在更新时期完全传播后才发布原子根向量，枚举线程固定一个已发布的根向量并遍历不可变版本，而后续更新则继续进行。
英文摘要
Continuous graph-query engines must process edge updates while making current query results available to concurrent consumers. Existing dynamic constant-delay enumeration methods provide strong per-answer delay guarantees, but are commonly formulated as a maintenance-then-enumeration process. Conversely, multicore graph-stream engines emphasize update throughput and match discovery without a query-level snapshot guarantee for concurrent full-result enumeration. We present TrieGS, a shared-memory engine that maintains a query-specific CDE state over a streaming graph. Logically, TrieGS uses the classical free-connex witness-subtree enumerator; dynamically, it maintains multiplicity payloads using exact signed deltas; physically, RDF terms are dictionary-encoded and the required access structures are realized as versioned LFNT relations (Lock-Free Nested Trie). The new systems problem is not relation-level snapshotting alone: an enumeration job must observe one consistent state across all interdependent base relations, projections, views, and indexes. TrieGS therefore publishes an atomic root vector only after an update epoch has fully propagated. Enumeration threads pin one published root vector and traverse immutable versions while later updates continue.
