Time-Series Bitmaps: A Practical Visualization Tool for Working with Large Time Series Databases. Kumar, N., Lolla, V., & Keogh, E.
Time-Series Bitmaps: A Practical Visualization Tool for Working with Large Time Series Databases [link]Paper  doi  abstract   bibtex   
The increasing interest in time series data mining in the last decade has resulted in the introduction of a variety of similarity measures, representations and algorithms. Surprisingly, this massive research effort has had little impact on real world applications. Real world practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work we attempt to address this problem by introducing a simple parameter-light tool that allows users to efficiently navigate through large collections of time series. Our system has the unique advantage that it can be embedded directly into the any standard graphical user interface, such as Microsoft Windows, thus making deployment easier. Our approach extracts features from a time series of arbitrary length, and uses information about the relative frequency of its features to color a bitmap in a principled way. By visualizing the similarities and differences within a collection of bitmaps, a user can quickly discover clusters, anomalies, and other regularities within their data collection. We demonstrate the utility of our approach with a set of comprehensive experiments on real datasets from a variety of domains
@article{kumarTimeseriesBitmapsPractical2005,
  title = {Time-Series {{Bitmaps}}: A {{Practical Visualization Tool}} for {{Working}} with {{Large Time Series Databases}}},
  url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611972757.55},
  doi = {http://dx.doi.org/10.1137/1.9781611972757.55 Book Code: PR119 Series: Proceedings Pages: 5 Read More: http://epubs.siam.org/doi/abs/10.1137/1.9781611972757.55},
  abstract = {The increasing interest in time series data mining in the last decade has resulted in the introduction of a variety of similarity measures, representations and algorithms. Surprisingly, this massive research effort has had little impact on real world applications. Real world practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work we attempt to address this problem by introducing a simple parameter-light tool that allows users to efficiently navigate through large collections of time series. Our system has the unique advantage that it can be embedded directly into the any standard graphical user interface, such as Microsoft Windows, thus making deployment easier. Our approach extracts features from a time series of arbitrary length, and uses information about the relative frequency of its features to color a bitmap in a principled way. By visualizing the similarities and differences within a collection of bitmaps, a user can quickly discover clusters, anomalies, and other regularities within their data collection. We demonstrate the utility of our approach with a set of comprehensive experiments on real datasets from a variety of domains},
  journaltitle = {SIAM International Conference on Data Mining},
  date = {2005},
  pages = {531--535},
  keywords = {Chaos Game,Time Series,Visualization},
  author = {Kumar, Nitin and Lolla, Vn and Keogh, Eamonn},
  file = {/home/dimitri/Nextcloud/Zotero/storage/SW9YUMIR/Kumar Venkata et al. - Unknown - Time-series Bitmaps a Practical Visualization Tool for Working with Large Time Series Databases.pdf}
}
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