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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 (CC BY 3.0).
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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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