Galen Reeves, Jie Liu, Suman Nath, and Feng Zhao
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
|Published in||VLDB '2009: Proceedings of 35th Conference on Very Large Data Bases|
|Publisher||Very Large Data Bases Endowment Inc.|
All articles published in this journal are protected by copyright, which covers the exclusive rights to reproduce and distribute the article (e.g., as offprints), as well as all translation rights. No material published in this journal may be reproduced photographically or stored on microfilm, in electronic data bases, video disks, etc., without first obtaining written permission from Very Large Data Bases Endowment Inc.