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 |
| _version_ | 1848758249642262528 |
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| author | Arandjelovic, O. Pham, DucSon Venkatesh, S. |
| author2 | Loo, C.K. |
| author_facet | Loo, C.K. Arandjelovic, O. Pham, DucSon Venkatesh, S. |
| author_sort | Arandjelovic, O. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:40:59Z |
| format | Conference Paper |
| id | curtin-20.500.11937-49483 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:40:59Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-494832023-02-27T07:34:26Z Stream Quantiles via Maximal Entropy Histograms Arandjelovic, O. Pham, DucSon Venkatesh, S. Loo, C.K. Keem Siah, Y. Wong, K.K.W. Beng Jin, A.T. Huang, K. video surveillance entropy stream data histogram algorithms quantile estimation 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/49483 10.1007/978-3-319-12640-1_40 Springer restricted |
| spellingShingle | video surveillance entropy stream data histogram algorithms quantile estimation Arandjelovic, O. Pham, DucSon Venkatesh, S. Stream Quantiles via Maximal Entropy Histograms |
| title | Stream Quantiles via Maximal Entropy Histograms |
| title_full | Stream Quantiles via Maximal Entropy Histograms |
| title_fullStr | Stream Quantiles via Maximal Entropy Histograms |
| title_full_unstemmed | Stream Quantiles via Maximal Entropy Histograms |
| title_short | Stream Quantiles via Maximal Entropy Histograms |
| title_sort | stream quantiles via maximal entropy histograms |
| topic | video surveillance entropy stream data histogram algorithms quantile estimation |
| url | http://hdl.handle.net/20.500.11937/49483 |