The rapid expansion of video streaming content in our daily lives has rendered the real-time processing and analysis of these video streams a critical capability. However, existing deep video analytics systems primarily support only simple queries, such as selection and aggregation. Considering the inherent temporal nature of video streams, queries capable of matching patterns of events could enable a wider range of applications. In this paper, we present Bobsled, a novel video stream processing system designed to efficiently support complex event queries. Experimental results demonstrate that Bobsled can achieve a throughput improvement over state-of-the-art ranging from 2.4× to 11.6×, without any noticeable loss in accuracy.

Tail latency is crucial as it impacts user satisfaction and service-level objectives (SLOs). However, geo-distributed databases have long struggled with this issue due to wide-area network access, resulting in tail latencies of several or even exceeding ten seconds. In this paper, we highlight that further optimizing atomic commit protocols does not help but hit a tail latency wall. Instead, making concurrency control and access method selection geo-aware can mitigate this issue. To this end, we present Bonspiel, a new geo-distributed database equipped with geo-aware concurrency control and access method selection. In our experiments, Bonspiel successfully caps the tail latency of TPC-C at 1.8 seconds. Remarkably, it achieves this while maintaining full generality - it is fully SQL-compliant and strongly consistent, with both average latency and system throughput remaining at the top of the field.