Stream Quantiles via Maximal Entropy Histograms
We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/49483 |
| Summary: | We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large real-world data sets demonstrate that the proposed methods vastly outperform theexisting alternatives. |
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