Compressing Historical Information in Sensor Networks
Summary: Compressing multi-stream historical sensor data using a base signal to encode piecewise linear correlations across measurements. Efficient feature extraction and encoding yield data reduction and outperform Wavelets, Histograms, and DCT on real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Antonios Deligiannakis (AT&T; University of Maryland)
- 2. Yannis Kotidis (AT&T)
- 3. Nick Roussopoulos (University of Maryland)
BibTeX Citation
@inproceedings{deligiannakis_sigmod04,
title = {{Compressing Historical Information in Sensor Networks}},
author = {Deligiannakis, Antonios and Kotidis, Yannis and Roussopoulos, Nick},
series = {{SIGMOD} '04},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1007568.1007628},
url = {https://dl.acm.org/doi/10.1145/1007568.1007628},
year = {2004}
}
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