International audienceWe propose a new probabilistic algorithm to find the top-k most recent and frequent items in distributed streams. This algorithm significantly improves upon the reliability and accuracy of existing results, while significantly reducing the memory footprint needed by each of the distributed nodes to solve this problem
Abstract. The problem of identifying the most frequent items across multiple datasets has received c...
Mining frequent itemsets has been widely studied over the last decade, mostly focuses on mining freq...
Abstract Most of the traditional top-k algorithms are based on a single-server setting. They may be ...
International audienceWe propose a new probabilistic algorithm to find the top-k most recent and fre...
We study the problem of finding the k most frequent items in a stream of items for the recently prop...
International audienceThis paper presents a new algorithm that detects on the fly the k most frequen...
The frequent items problem is to process a stream of items and find all items occurring more than a ...
AbstractWe present a 1-pass algorithm for estimating the most frequent items in a data stream using ...
We consider the problem of maintaining frequency counts for items occurring frequently in the union ...
A data stream is a massive unbounded sequence of data elements continuously gen-erated at a rapid ra...
GDD_HCERES2020Estimating the frequency of any piece of information in large-scale distributed data s...
International audienceEstimating the frequency of any piece of information in large-scale distribute...
Abstract. The problem of frequent item discovery in streaming data has attracted a lot of attention ...
We propose an approximate integrated approach for solving both problems of finding the most pop-ular...
We consider the problem of maintaining frequency counts for items occurring frequently in the union ...
Abstract. The problem of identifying the most frequent items across multiple datasets has received c...
Mining frequent itemsets has been widely studied over the last decade, mostly focuses on mining freq...
Abstract Most of the traditional top-k algorithms are based on a single-server setting. They may be ...
International audienceWe propose a new probabilistic algorithm to find the top-k most recent and fre...
We study the problem of finding the k most frequent items in a stream of items for the recently prop...
International audienceThis paper presents a new algorithm that detects on the fly the k most frequen...
The frequent items problem is to process a stream of items and find all items occurring more than a ...
AbstractWe present a 1-pass algorithm for estimating the most frequent items in a data stream using ...
We consider the problem of maintaining frequency counts for items occurring frequently in the union ...
A data stream is a massive unbounded sequence of data elements continuously gen-erated at a rapid ra...
GDD_HCERES2020Estimating the frequency of any piece of information in large-scale distributed data s...
International audienceEstimating the frequency of any piece of information in large-scale distribute...
Abstract. The problem of frequent item discovery in streaming data has attracted a lot of attention ...
We propose an approximate integrated approach for solving both problems of finding the most pop-ular...
We consider the problem of maintaining frequency counts for items occurring frequently in the union ...
Abstract. The problem of identifying the most frequent items across multiple datasets has received c...
Mining frequent itemsets has been widely studied over the last decade, mostly focuses on mining freq...
Abstract Most of the traditional top-k algorithms are based on a single-server setting. They may be ...