2010 DUSTaGeneralizedNotionofSimilar

• (Sarangi et al., 2010) ⇒ Smruti R. Sarangi, and Karin Murthy. (2010). “DUST: A Generalized Notion of Similarity Between Uncertain Time Series.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835854

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• http://scholar.google.com/scholar?q=%22DUST%3A+a+generalized+notion+of+similarity+between+uncertain+time+series%22+2010
• http://portal.acm.org/citation.cfm?id=1835854&preflayout=flat#citedby

Quotes

Abstract

Large-scale sensor deployments and an increased use of privacy-preserving transformations have led to an increasing interest in mining uncertain time series data. Traditional distance measures such as Euclidean distance or dynamic time warping are not always effective for analyzing uncertain time series data. Recently, some measures have been proposed to account for uncertainty in time series data. However, we show in this paper that their applicability is limited. In specific, these approaches do not provide an intuitive way to compare two uncertain time series and do not easily accommodate multiple error functions.

In this paper, we provide a theoretical framework that generalizes the notion of similarity between uncertain time series. Secondly, we propose DUST, a novel distance measure that accommodates uncertainty and degenerates to the Euclidean distance when the distance is large compared to the error. We provide an extensive experimental validation of our approach for the following applications: classification, top-k motif search, and top-k nearest-neighbor queries.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 DUSTaGeneralizedNotionofSimilar	Smruti R. Sarangi Karin Murthy			DUST: A Generalized Notion of Similarity Between Uncertain Time Series		KDD-2010 Proceedings		10.1145/1835804.1835854		2010