23,399 research outputs found
Fast and Accurate Mining of Correlated Heavy Hitters
The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional
data stream has been introduced recently, and a deterministic algorithm based
on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to
solve it. In this paper we present a new counter-based algorithm for tracking
CHHs, formally prove its error bounds and correctness and show, through
extensive experimental results, that our algorithm outperforms the Misra--Gries
based algorithm with regard to accuracy and speed whilst requiring
asymptotically much less space
Optimal Elephant Flow Detection
Monitoring the traffic volumes of elephant flows, including the total byte
count per flow, is a fundamental capability for online network measurements. We
present an asymptotically optimal algorithm for solving this problem in terms
of both space and time complexity. This improves on previous approaches, which
can only count the number of packets in constant time. We evaluate our work on
real packet traces, demonstrating an up to X2.5 speedup compared to the best
alternative.Comment: Accepted to IEEE INFOCOM 201
Simple and Deterministic Matrix Sketching
We adapt a well known streaming algorithm for approximating item frequencies
to the matrix sketching setting. The algorithm receives the rows of a large
matrix one after the other in a streaming fashion. It
maintains a sketch matrix B \in \R^ {1/\eps \times m} such that for any unit
vector [\|Ax\|^2 \ge \|Bx\|^2 \ge \|Ax\|^2 - \eps \|A\|_{f}^2 \.] Sketch
updates per row in require O(m/\eps^2) operations in the worst case. A
slight modification of the algorithm allows for an amortized update time of
O(m/\eps) operations per row. The presented algorithm stands out in that it
is: deterministic, simple to implement, and elementary to prove. It also
experimentally produces more accurate sketches than widely used approaches
while still being computationally competitive
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