
Plotly-resampler : effective visual analytics for large time series
- Author
- Jonas Van Der Donckt (UGent) , Jeroen Van Der Donckt (UGent) , Emiel Deprost (UGent) and Sofie Van Hoecke (UGent)
- Organization
- Project
- Abstract
- Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.
- Keywords
- VISUALIZATION, Time series, Visual analytics, Python, Dash Plotly, Open source
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GRTYD8HDQZ4YDMES8MF6QG7Q
- MLA
- Van Der Donckt, Jonas, et al. “Plotly-Resampler : Effective Visual Analytics for Large Time Series.” 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS), IEEE, 2022, pp. 21–25, doi:10.1109/VIS54862.2022.00013.
- APA
- Van Der Donckt, J., Van Der Donckt, J., Deprost, E., & Van Hoecke, S. (2022). Plotly-resampler : effective visual analytics for large time series. 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS), 21–25. https://doi.org/10.1109/VIS54862.2022.00013
- Chicago author-date
- Van Der Donckt, Jonas, Jeroen Van Der Donckt, Emiel Deprost, and Sofie Van Hoecke. 2022. “Plotly-Resampler : Effective Visual Analytics for Large Time Series.” In 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS), 21–25. IEEE. https://doi.org/10.1109/VIS54862.2022.00013.
- Chicago author-date (all authors)
- Van Der Donckt, Jonas, Jeroen Van Der Donckt, Emiel Deprost, and Sofie Van Hoecke. 2022. “Plotly-Resampler : Effective Visual Analytics for Large Time Series.” In 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS), 21–25. IEEE. doi:10.1109/VIS54862.2022.00013.
- Vancouver
- 1.Van Der Donckt J, Van Der Donckt J, Deprost E, Van Hoecke S. Plotly-resampler : effective visual analytics for large time series. In: 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS). IEEE; 2022. p. 21–5.
- IEEE
- [1]J. Van Der Donckt, J. Van Der Donckt, E. Deprost, and S. Van Hoecke, “Plotly-resampler : effective visual analytics for large time series,” in 2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS), Oklahoma City, OK, 2022, pp. 21–25.
@inproceedings{01GRTYD8HDQZ4YDMES8MF6QG7Q, abstract = {{Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.}}, author = {{Van Der Donckt, Jonas and Van Der Donckt, Jeroen and Deprost, Emiel and Van Hoecke, Sofie}}, booktitle = {{2022 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS)}}, isbn = {{9781665488129}}, issn = {{2771-9537}}, keywords = {{VISUALIZATION,Time series,Visual analytics,Python,Dash Plotly,Open source}}, language = {{eng}}, location = {{Oklahoma City, OK}}, pages = {{21--25}}, publisher = {{IEEE}}, title = {{Plotly-resampler : effective visual analytics for large time series}}, url = {{http://doi.org/10.1109/VIS54862.2022.00013}}, year = {{2022}}, }
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