Ilari Shafer, Raja R. Sambasivan, Anthony Rowe, and Gregory R. Ganger, Carnegie Mellon University
Abstract:
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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BibTeX
@inproceedings {179262,
author = {Ilari Shafer and Raja R. Sambasivan and Anthony Rowe and Gregory R. Ganger},
title = {Specialized Storage for Big Numeric Time Series},
booktitle = {5th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 13)},
year = {2013},
address = {San Jose, CA},
url = {https://www.usenix.org/conference/hotstorage13/workshop-program/presentation/shafer},
publisher = {USENIX Association},
month = jun
}
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