Towards Efficient MaxBRNN Computation for Streaming Updates

Wentao Ning, Xiao Yan, Bo Tang

Abstract: In this paper, we propose the streamingMaxBRNNquery, which finds the optimal region to deploy a new service point when both the service points and client points are under continuous updates. The streaming MaxBRNNquery has many applications such as taxi scheduling, shared bike placements, etc. Existing MaxBRNNsolutions are insufficient for streaming updates as they need to re-run from scratch even for a small amount of updates, resulting in long query processing time. To tackle this problem, we devise an efficient slot partitioning-based algorithm (SlotP), which divides the space into equal-sized slots and processes each slot independently. The superiorities of our proposal for streaming MaxBRNNquery are: (i) an update affects only a smaller number of slots and works done on the unaffected slots can be reused directly; (ii) the influence value upper bound of each slot can be derived efficiently and accurately, which facilitate pruning many slots from expensive computation. We conducted extensive experiments to validate the performance of the SlotPalgorithm. The results show that SlotPis 2-3 orders of magnitude faster than state-of-the-art baselines.