Managing massive time series streams with multi-scale compressed trickles

Published

Journal Article

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. © 2009 VLDB Endowment.

Full Text

Duke Authors

Cited Authors

  • Reeves, G; Liu, J; Nath, S; Zhao, F

Published Date

  • January 1, 2009

Published In

Volume / Issue

  • 2 / 1

Start / End Page

  • 97 - 108

Electronic International Standard Serial Number (EISSN)

  • 2150-8097

Digital Object Identifier (DOI)

  • 10.14778/1687627.1687639

Citation Source

  • Scopus