IEEE VIS 2024 Content: FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

FPCS: Feature Preserving Compensated Sampling of Streaming Time Series Data

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Hongyan Li - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

 Bo Yang - China Nanhu Academy of Electronics and Information Technology(CNAEIT), JiaXing, China

 Yansong Chua - China Nanhu Academy of Electronics and Information Technology, Jiaxing, China

 Room: Palma Ceia I

2024-10-16T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T12:30:00Z

Exemplar figure, described by caption below — The FPCS algorithm is used to sample streaming time series data. Each row corresponds to one of the five typical datasets. Columns 1-4 represent the visualization fitting effect of the first 100,000 data points in these datasets using the newly proposed FPCS and the other three algorithms, based on a 100:1 sampling ratio. The red line represents original data points; the green line represents sampled data points. Column 5 uses SSIM to compare the sampling effects of the four algorithms based on sampling ratios of 100:1, 200:1, 500:1, and 1000:1. The FPCS algorithm shows the best sampling results and performance.

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Keywords

Data visualization, Massive, Streaming, Time series, Line charts, Sampling, Feature, Compensating

Abstract

Data visualization aids in making data analysis more intuitive and in-depth, with widespread applications in fields such as biology, finance, and medicine. For massive and continuously growing streaming time series data, these data are typically visualized in the form of line charts, but the data transmission puts significant pressure on the network, leading to visualization lag or even failure to render completely. This paper proposes a universal sampling algorithm FPCS, which retains feature points from continuously received streaming time series data, compensates for the frequent fluctuating feature points, and aims to achieve efficient visualization. This algorithm bridges the gap in sampling for streaming time series data. The algorithm has several advantages: (1) It optimizes the sampling results by compensating for fewer feature points, retaining the visualization features of the original data very well, ensuring high-quality sampled data; (2) The execution time is the shortest compared to similar existing algorithms; (3) It has an almost negligible space overhead; (4) The data sampling process does not depend on the overall data; (5) This algorithm can be applied to infinite streaming data and finite static data.