The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
i SAX: indexing and mining terabyte sized time series
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.
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Cites
Fast subsequence matching in time-series databases
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Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
Self-citationDimensionality Reduction for Fast Similarity Search in Large Time Series Databases
Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011669 · Cited by 1,573
Self-citationLocally adaptive dimensionality reduction for indexing large time series databases
Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001) · Proceedings of the 2001 ACM SIGMOD international conference on Management of data · doi:10.1145/375663.375680 · Cited by 866
Self-citationFast time series classification using numerosity reduction
Xi, X., Keogh, E., Shelton, C., Wei, L., & Ratanamahatana, C. A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143974 · Cited by 591
Self-citationThree Myths about Dynamic Time Warping Data Mining
Ratanamahatana, C. A., & Keogh, E. (2005) · Proceedings of the 2005 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.50 · Cited by 393
Self-citationIndexing spatio-temporal trajectories with Chebyshev polynomials
Cai, Y., & Ng, R. (2004) · Proceedings of the 2004 ACM SIGMOD international conference on Management of data · doi:10.1145/1007568.1007636 · Cited by 309
Discovery of climate indices using clustering
Steinbach, M. S., Tan, P., Kumar, V., Klooster, S. A., & Potter, C. S. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956801 · Cited by 148
A wavelet transform-based ECG compression method guaranteeing desired signal quality
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Compression of ECG signals by optimized quantization of discrete cosine transform coefficients
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Cited by
Searching and mining trillions of time series subsequences under dynamic time warping
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., & Keogh, E. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339576 · Cited by 1,047
Self-citationHammerla, N. Y., Kirkham, R., Andras, P., & Ploetz, T. (2013) · Proceedings of the 2013 International Symposium on Wearable Computers · doi:10.1145/2493988.2494353 · Cited by 138
MAGIC 2.0: A web tool for false positive prediction and prevention for gesture recognition systems
Kohlsdorf, D., Starner, T., & Ashbrook, D. (2011) · Face and Gesture 2011 · doi:10.1109/fg.2011.5771412 · Cited by 24
Ordonez, P., Armstrong, T., Oates, T., & Fackler, J. (2011) · 2011 10th International Conference on Machine Learning and Applications and Workshops · doi:10.1109/icmla.2011.46 · Cited by 8
Metadata
- Authors
- Shieh, J., & Keogh, E.
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 623-631
- OpenAlex
- W2077720176
- Cited by
- 323
- References
- 15
- FWCI
- 16.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shieh, J., & Keogh, E. (2008). i SAX: indexing and mining terabyte sized time series. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1401890.1401966
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
