@inproceedings{75686863ca38415396c7fda563d35306,
title = "Sampling from dense streams without penalty improved bounds for frequency moments and heavy hitters",
abstract = "We investigate the ability to sample relatively small amounts of data from a stream and approximately calculate statistics on the original stream. McGregor et al. [29] provide worst case theoretical bounds that show space costs for sampling that are inversely correlated with the sampling rate. Indeed, while the lower bound of McGregor et al. cannot be improved in the general case, we show it is possible to improve the space bound for stream D of domain n, when the average positive frequency μ=F 1/F0 is sufficiently large. We consider the following range of parameters: μ≥log(n) and sample rate p≥Ckμ-1log(n), where Ck is a constant. On these streams we improve the bound from {\~O}(1/pn 1-2/k) to thus giving polynomial improvement in space for sufficiently large μ and p -1.",
keywords = "Frequency Moments, Heavy Hitters, Sampling, Streaming Algorithms",
author = "Vladimir Braverman and Gregory Vorsanger",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-08783-2\_2",
language = "English",
isbn = "9783319087825",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "13--24",
booktitle = "Computing and Combinatorics - 20th International Conference, COCOON 2014, Proceedings",
address = "Germany",
note = "20th International Computing and Combinatorics Conference, COCOON 2014 ; Conference date: 04-08-2014 Through 06-08-2014",
}