Abstract
In most networking applications such as IoT (Internet of Things), data are being generated at a high rate so that the long–term storage cost outweighs its benefits. Such streams of data are stored temporarily, and should be mined fast before they are lost forever. In a previous work, we have presented Time–decaying Bloom Filters (TBF) for maintaining time–varying frequency statistics in data streams. TBF extends the standard Bloom Filters (for approximate membership queries) by replacing the bit-vector with an array of small counters, whose values decay periodically with time. In this paper, we consider hot spot tracking problem for data streams. To this problem, we propose a novel scheme by integrating TBF and online sampling technology. Data streams are sampled in an online manner using a reservoir. Items newly sampled are passed to a TBF where frequency statistics are maintained for hot spot reporting.
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Cheng, K. (2020). Hot Spot Tracking by Time-Decaying Bloom Filters and Reservoir Sampling. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_96
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DOI: https://doi.org/10.1007/978-3-030-15032-7_96
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