In this paper, we give a simple scheme for identifying ε-approximate frequent items over a sliding window of size n. Our scheme is deterministic and does not make any assumption on the distribution of the item frequencies. It supports O(1/ε) update and query time, and uses O(1/ε) space. It is very simple; its main data structures are just a few short queues whose entries store the position of some items in the sliding window. We also extend our scheme for variable-size window. This extended scheme uses O(1/ε log(εn)) space. Copyright 2006 ACM.link_to_subscribed_fulltex
This paper presents algorithms for estimating aggregate functions over a “sliding window ” of the N ...
National audienceWe introduce the problem of mining frequent sequences in a window sliding over a st...
International audienceThis paper presents a new algorithm that detects on the fly the k most frequen...
In this paper, we consider the sliding window model and propose two different (on-line) algorithms t...
We consider the problem of maintaining -approximate counts and quantiles over a stream sliding windo...
The results of this paper are superceded by the paper at: http://arxiv.org/abs/1309.3690. We conside...
In an asynchronous data stream, the data items may be out of order with respect to their original ti...
We study the problem of finding the k most frequent items in a stream of items for the recently prop...
Frequent-pattern discovery in data streams is more challenging than that in traditional databases si...
We study the problem of identifying items with heavy weights in the sliding window of a weighted dat...
Abstract. We propose a false-negative approach to approximate the set of frequent itemsets (FIs) ove...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
LNCS v. 5893 is Proceedings of the 7th International Workshop, WAOA 2009WAOA Session 7In an asynchro...
Frequent itemset mining over sliding window is an interesting problem and has a large number of appl...
This paper presents algorithms for estimating aggregate functions over a “sliding window ” of the N ...
National audienceWe introduce the problem of mining frequent sequences in a window sliding over a st...
International audienceThis paper presents a new algorithm that detects on the fly the k most frequen...
In this paper, we consider the sliding window model and propose two different (on-line) algorithms t...
We consider the problem of maintaining -approximate counts and quantiles over a stream sliding windo...
The results of this paper are superceded by the paper at: http://arxiv.org/abs/1309.3690. We conside...
In an asynchronous data stream, the data items may be out of order with respect to their original ti...
We study the problem of finding the k most frequent items in a stream of items for the recently prop...
Frequent-pattern discovery in data streams is more challenging than that in traditional databases si...
We study the problem of identifying items with heavy weights in the sliding window of a weighted dat...
Abstract. We propose a false-negative approach to approximate the set of frequent itemsets (FIs) ove...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
LNCS v. 5893 is Proceedings of the 7th International Workshop, WAOA 2009WAOA Session 7In an asynchro...
Frequent itemset mining over sliding window is an interesting problem and has a large number of appl...
This paper presents algorithms for estimating aggregate functions over a “sliding window ” of the N ...
National audienceWe introduce the problem of mining frequent sequences in a window sliding over a st...
International audienceThis paper presents a new algorithm that detects on the fly the k most frequen...