Stream Aggregation Through Order Sampling Conference Paper uri icon


  • 2017 Copyright held by the owner/author(s). This paper introduces a newsingle-pass reservoirweighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA). While order sampling is a powerful and efficient method forweighted sampling from a stream of uniquely keyed items, there is no current algorithm that realizes the bene.fits of order sampling in the context of stream aggregation over non-unique keys. A naive approach to order sample regardless of key then aggregate the results is hopelessly inefficient. In distinction, our proposed algorithm uses a single persistent random variable across the lifetime of each key in the cache, and maintains unbiased estimates of the key aggregates that can be queried at any point in the stream. The basic approach can be supplemented with a Sample and Hold pre-sampling stage with a sampling rate adaptation controlled by PBA. .is approach represents a considerable reduction in computational complexity compared with the state of the art in adapting Sample and Hold to operate with a .xed cache size. Concerning statistical properties, we prove that PBA provides unbiased estimates of the true aggregates. We analyze the computational complexity of PBA and its variants, and provide a detailed evaluation of its accuracy on synthetic and trace data. Weighted relative error is reduced by 40% to 65% at sampling rates of 5% to 17%, relative to Adaptive Sample and Hold; there is also substantial improvement for rank queries.

name of conference

  • Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

published proceedings


author list (cited authors)

  • Duffield, N., Xu, Y., Xia, L., Ahmed, N. K., & Yu, M.

citation count

  • 3

complete list of authors

  • Duffield, Nick||Xu, Yunhong||Xia, Liangzhen||Ahmed, Nesreen K||Yu, Minlan

publication date

  • November 2017