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In many data stream oriented application circumstances, frequency distribution of elements meets heavy-tailed distribution, which means most elements have small frequencies and few elements have high frequencies. Consequently, traditional counting bloom filters (CBF) and dynamic count filters (DCF) cannot represent the data effectively. In order to improve the data processing efficiency, this paper...
We study the problem of filtering duplicate items over physically distributed data streams to provide clean data for real-time monitoring applications. Existing approaches only filter local duplicates within each stream, and their space and time costs are hardly feasible for high-speed data streams. Based on the space/time efficient data structure Bloom filter, we propose a novel local filtering algorithm...
Burst detection is an inherent problem for data streams, so it has attracted extensive attention in research community due to its broad applications. One of the basic problems in burst detection is how to count frequencies of all elements in data stream. This paper presents a novel solution based on Improved Counting Bloom Filter, which is also called BCBF+HSet. Comparing with intuitionistic approach...
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