Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features

Xiaolan Wang, Kasim Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time-series along with external knowledge, including variate relationships, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.

Original languageEnglish (US)
Title of host publication2014 IEEE 30th International Conference on Data Engineering, ICDE 2014
PublisherIEEE Computer Society
Pages388-399
Number of pages12
ISBN (Print)9781479925544
DOIs
StatePublished - Jan 1 2014
Event30th IEEE International Conference on Data Engineering, ICDE 2014 - Chicago, IL, United States
Duration: Mar 31 2014Apr 4 2014

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other30th IEEE International Conference on Data Engineering, ICDE 2014
CountryUnited States
CityChicago, IL
Period3/31/144/4/14

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Wang, X., Candan, K., & Sapino, M. L. (2014). Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features. In 2014 IEEE 30th International Conference on Data Engineering, ICDE 2014 (pp. 388-399). [6816667] (Proceedings - International Conference on Data Engineering). IEEE Computer Society. https://doi.org/10.1109/ICDE.2014.6816667