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.[], who gave an O (1∊ log W log (∊ B log W) min {log W, 1∊} log| U|)-space
data structure that can approximate the frequent items within an∊ error bound, where W and …

In an asynchronous data stream, the data items may be out of order with respect to their
original timestamps. This paper gives a space-efficient data structure to maintain such a data
stream so that it can approximate the frequent item set over a sliding time window with
sufficient accuracy. Prior to our work, Cormode et al. 3 have the best solution, with space
complexity O(1ε\logW\log(εB\logW)\min{\logW,1ε\}\logU), where ε is the given error bound, W
and B are parameters of the sliding window, and U is the number of all possible item names …