PDL ABSTRACT

Specialized Storage for Big Numeric Time Series

Proceedings of the 5th Workshop on Hot Topics in Storage and File Systems, June 2013.

Ilari Shafer, Raja R. Sambasivan, Anthony Rowe, Gregory R. Ganger

Carnegie Mellon University
Pittsburgh, PA 15213

http://www.pdl.cmu.edu/

Numeric time series data has unique storage requirements and access patterns that can benefit from specialized support, given its importance in Big Data analyses. Popular frameworks and databases focus on addressing other needs, making them a suboptimal fit. This paper describes the support needed for numeric time series, suggests an architecture for efficient time series storage, and illustrates its potential for satisfying key requirements.

FULL PAPER: pdf

 

 

 

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