无连接的变化传播
Change Propagation Without Joins
AI总结：
本文提出一种无连接的变化传播框架，避免标准框架中因连接操作符导致的多项式空间和时间开销，同时保持常数延迟枚举，并给出更新代价分析和查询计划优化方法，实验证明其系统原型在空间、时间和延迟上显著优于现有系统。
摘要：
我们重新审视了更新下查询求值的经典变化传播框架。标准框架采用查询计划并物化中间视图，这在空间和时间上都会产生高昂的多项式代价，其中连接操作符是罪魁祸首。在本文中，我们提出了一种无连接的新变化传播框架，从而自然地避免了这种多项式爆炸。同时，我们证明新框架仍然支持对增量结果和完整查询结果的常数延迟枚举，与标准框架相同。此外，我们对其更新代价进行了定量分析，这不仅恢复了许多关于该问题的近期理论结果，还产生了一种优化查询计划的有效方法。新框架也易于集成到现有的流数据库系统中。实验结果表明，我们使用 Flink DataStream API 实现的系统原型在空间、时间和延迟方面显著优于其他系统。
英文摘要：
We revisit the classical change propagation framework for query evaluation under updates. The standard framework takes a query plan and materializes the intermediate views, which incurs high polynomial costs in both space and time, with the join operator being the culprit. In this paper, we propose a new change propagation framework without joins, thus naturally avoiding this polynomial blowup. Meanwhile, we show that the new framework still supports constant-delay enumeration of both the deltas and the full query results, the same as in the standard framework. Furthermore, we provide a quantitative analysis of its update cost, which not only recovers many recent theoretical results on the problem, but also yields an effective approach to optimizing the query plan. The new framework is also easy to be integrated into an existing streaming database system. Experimental results show that our system prototype, implemented using Flink DataStream API, significantly outperforms other systems in terms of space, time, and latency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
