Mining frequent items in the time fading model

We introduce FDCMSS, a novel sketch-based algorithm for frequent items working in the time fading model. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms.We formally prove the correctness of our algorithm.We experimentally validate the algorithm on synthetic data distributed using a Zipf distribution, and also on real datasets.We compare the performances and the error committed by our algorithm against λ-HCount, an algorithm recently proposed by Chen and Mei. Extensive experimental results show that FDCMSS outperforms λ-HCount with regard to speed, space used, precision attained and error committed on both synthetic and real datasets. We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms λ-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.