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
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.
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