2008 ISAXIndexingandMiningTerabyteSi

• (Shieh et al., 2008) ⇒ Jin Shieh, and Eamonn Keogh. (2008). “I SAX: Indexing and Mining Terabyte Sized Time Series.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401966

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22+i+SAX%3A+indexing+and+mining+terabyte+sized+time+series%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401966&preflayout=flat#citedby

Quotes

Abstract

Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we show how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. Our approach allows both fast exact search and ultra fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing millions of time series.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ISAXIndexingandMiningTerabyteSi	Eamonn Keogh Jin Shieh			I SAX: Indexing and Mining Terabyte Sized Time Series				10.1145/1401890.1401966