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Algorithms 2011, 4(3), 200-222; doi:10.3390/a4030200
Article

Approximating Frequent Items in Asynchronous Data Stream over a Sliding Window

1,* , 2
, 1
 and 1
1 Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, China 2 MADALGO (Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation), Department of Computer Science, Aarhus University, Aarhus C DK-8000, Denmark
* Author to whom correspondence should be addressed.
Received: 23 June 2011 / Revised: 23 June 2011 / Accepted: 10 September 2011 / Published: 22 September 2011
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Abstract

In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of frequent items over a sliding time window with sufficient accuracy. Prior to our work, the best solution is given by Cormode et al. [1], who gave an O (1/ε log W log (εB/ log W) min {log W, 1/εlog |U|)- space data structure that can approximate the frequent items within an ε error bound, where W and B are parameters of the sliding window, and U is the set of all possible item names. We gave a more space-efficient data structure that only requires O (1/ε log W log (εB/ logWlog log W) space.
Keywords: asynchronous data streams; frequent items; sliding window; space complexity asynchronous data streams; frequent items; sliding window; space complexity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Ting, H.-F.; Lee, L.-K.; Chan, H.-L.; Lam, T.-W. Approximating Frequent Items in Asynchronous Data Stream over a Sliding Window. Algorithms 2011, 4, 200-222.

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