Distributed streams algorithms for sliding windows

  • Authors:
  • Phillip B. Gibbons;Srikanta Tirthapura

  • Affiliations:
  • Intel Research Pittsburgh, Pittsburgh, PA;Brown University, Providence, RI

  • Venue:
  • Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
  • Year:
  • 2002

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Abstract

This paper presents algorithms for estimating aggregate functions over a "sliding window" of the N most recent data items in one or more streams. Our results include:For a single stream, we present the first &egr;-approximation scheme for the number of 1's in a sliding window that is optimal in both worst case time and space. We also present the first &egr; for the sum of integers in [0..R] in a sliding window that is optimal in both worst case time and space (assuming R is at most polynomial in N). Both algorithms are deterministic and use only logarithmic memory words.In contrast, we show that an deterministic algorithm that estimates, to within a small constant relative error, the number of 1's (or the sum of integers) in a sliding window over the union of distributed streams requires &OHgr;(N) space. We present the first randomized (&egr;,&sgr;)-approximation scheme for the number of 1's in a sliding window over the union of distributed streams that uses only logarithmic memory words. We also present the first (&egr;,&sgr;)-approximation scheme for the number of distinct values in a sliding window over distributed streams that uses only logarithmic memory words.