Managing Massive Time Series Streams with Multi-Scale Compressed Trickles

We present Cypress, a novel framework to archive and query

massive time series streams such as those generated by sensor

networks, data centers, and scientific computing. Cypress

applies multi-scale analysis to decompose time series

and to obtain sparse representations in various domains (e.g.

frequency domain and time domain). Relying on the sparsity,

the time series streams can be archived with reduced

storage space. We then show that many statistical queries

such as trend, histogram and correlations can be answered

directly from compressed data rather than from reconstructed

raw data. Our evaluation with server utilization data collected

from real data centers shows significant benefit of our

framework.

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In  VLDB '2009: Proceedings of 35th Conference on Very Large Data Bases

Publisher  Very Large Data Bases Endowment Inc.
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