流图上的增量滑动窗口连通性
Incremental Sliding Window Connectivity over Streaming Graphs
AI总结：
针对流图滑动窗口连通性查询中过期边删除代价高的问题，提出双向增量计算的 BIC 索引框架及存储、更新与查询优化，显著提升吞吐并降低延迟。
摘要：
我们研究流图滑动窗口内连通性查询的基于索引处理。这类查询用于确定两个顶点是否属于同一连通分量，是实时图数据处理中的基础操作，需要高吞吐量和低延迟。尽管利用面向完全动态连通性的数据结构的索引方法可以促进高效查询处理，但在窗口更新期间从窗口中删除过期边时，它们会遇到重大挑战。我们提出一种新颖的索引方法，无需实际执行边删除。这是通过一个独特的双向增量计算框架实现的，称为 BIC 模型。BIC 模型实现两种不同的增量计算，以计算窗口内的连通分量，分别沿时间线和逆时间线运行。随后合并这些计算，以高效计算窗口内的查询。我们提出用于优化索引存储、增量索引更新和高效查询处理的技术，以提升 BIC 的有效性。实验表明，与最先进的索引相比，BIC 实现了 14 倍的吞吐量提升，并将 P95 延迟最多降低 3900 倍。
英文摘要：
We study index-based processing for connectivity queries within sliding windows on streaming graphs. These queries, which determine whether two vertices belong to the same connected component, are fundamental operations in real-time graph data processing and demand high throughput and low latency. While indexing methods that leverage data structures for fully dynamic connectivity can facilitate efficient query processing, they encounter significant challenges with deleting expired edges from the window during window updates. We introduce a novel indexing approach that eliminates the need for physically performing edge deletions. This is achieved through a unique bidirectional incremental computation framework, referred to as the BIC model. The BIC model implements two distinct incremental computations to compute connected components within the window, operating along and against the timeline, respectively. These computations are then merged to efficiently compute queries in the window. We propose techniques for optimized index storage, incremental index updates, and efficient query processing to improve BIC effectiveness. Empirically, BIC achieves a 14$\times$ increase in throughput and a reduction in P95 latency by up to 3900$\times$ when compared to state-of-the-art indexes.
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