Estimating first frequency moment of data stream in nearly optimal space and time
Joint work with Sumit Ganguly.
Conference version : 12th Italian Conference on Theoretical Computer Science (ICTCS 2010).
Estimating the first moment of a data stream defined as F1 = \sum_i∈{1,2,...,n}|fi| to within 1 ± eps-relative error... more
Estimating the first moment of a data stream defined as F1 = \sum_i∈{1,2,...,n}|fi| to within 1 ± eps-relative error with high probability is a basic and influential problem in data stream
processing. A tight space bound of O(1/eps^2 log(mM)) is known from the work of [9]. However, all known algorithms for this problem require per-update stream processing time of Ω(1/eps^2), with the only exception being the algorithm of [6] that requires per-update processing time of O(log^2(mM)(log n)) albeit with sub-optimal space O(1/eps^3 log2(mM)).
In this paper, we present an algorithm for estimating F1 that achieves near-optimality in both space and update processing time. The space requirement is O(1/eps^2(log n+(log 1/eps) log(mM))) and the per-update processing time is O((log n) log(1/eps)).
Download (.pdf) A data stream-based evaluation framework for traffic information systems
Sandra Geisler, Christoph Quix, Stefan Schiffer.
A Data Stream-based Evaluation Framework for Traffic Information Systems.
In Proc. of the 1st ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS) 2010.
November 2, San Jose, CA, USA.