Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2005/04/07
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Journal
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Indian UGC (journal)
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Refrences20
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Citations41
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Jessica Lin Computer Science & Engineering Department, University of California, Riverside, CA, U.S.A.
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Eamonn Keogh Computer Science & Engineering Department, University of California, Riverside, CA, U.S.A.
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Stefano Lonardi Computer Science & Engineering Department, University of California, Riverside, CA, U.S.A.
Abstract
Cite
Lin, Jessica, et al. “Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases”. Information Visualization, vol. 4, no. 2, 2005, pp. 61-82, https://doi.org/10.1057/palgrave.ivs.9500089.
Lin, J., Keogh, E., & Lonardi, S. (2005). Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases. Information Visualization, 4(2), 61-82. https://doi.org/10.1057/palgrave.ivs.9500089
Lin, Jessica, Eamonn Keogh, and Stefano Lonardi. “Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases”. Information Visualization 4, no. 2 (2005): 61-82. https://doi.org/10.1057/palgrave.ivs.9500089.
Lin J, Keogh E, Lonardi S. Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases. Information Visualization. 2005;4(2):61-82.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Title | 2004 | |||
| Theoria Generate Delia Statistica | 1888 | |||
| Theoria Generate Delia Statistica | 2004 | |||
| Symbolic Representation and Retrieval of Moving Object Trajectories. | 2003 | |||
| The UCR Time Series Data Mining Archive. | 2002 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Real-time visual analytics for in-home medical rehabilitation of stroke patient—systematic review | Medical & Biological Engineering & Computing |
| 3 | 2022 |
| MDataEE: Analysis and Visualization of Multifactor Time Series Data | Journal of Computer-Aided Design & Computer Graphics | 2022 | ||
| Concurrent time-series selections using deep learning and dimension reduction | Knowledge-Based Systems |
| 8 | 2021 |
| An Empirical Study of Task-Specific Limitations of the Overview+Detail Technique for Interactive Time Series Analysis | Procedia Computer Science | 3 | 2021 | |
ABBA: adaptive Brownian bridge-based symbolic aggregation of time series | Data Mining and Knowledge Discovery |
| 9 | 2020 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 20
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Detecting Motifs in System Call Sequences and was published in 2007. The most recent citation comes from a 2022 study titled MDataEE: Analysis and Visualization of Multifactor Time Series Data. This article reached its peak citation in 2016, with 5 citations. It has been cited in 31 different journals, 6% of which are open access. Among related journals, the IEEE Transactions on Visualization and Computer Graphics cited this research the most, with 5 citations. The chart below illustrates the annual citation trends for this article.