2021
DOI: 10.1145/3511918
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Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

Abstract: We propose PASS , a O ( n ) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line chart. Visualization of large trend line data is a challenge and current sampling approaches do produce reduction but result in loss of semantics and anomalous behavior. We have evaluated PASS using 7 large and well-vetted datasets (Taxi, Temperature, D… Show more

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Cited by 2 publications
(1 citation statement)
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“…In this paper, we proposed a semantic preservation-based sequential data compression algorithm based on the data compression mainly for historical trust scores based on semantic preservation sequential data sampling algorithm [12] and the combination of low-density window merging operation. The algorithm to achieve the function is to delete the redundant data before and after the moment data change is not significant.…”
Section: Semantic Time Windows Based On Sequential Data Compressionmentioning
confidence: 99%
“…In this paper, we proposed a semantic preservation-based sequential data compression algorithm based on the data compression mainly for historical trust scores based on semantic preservation sequential data sampling algorithm [12] and the combination of low-density window merging operation. The algorithm to achieve the function is to delete the redundant data before and after the moment data change is not significant.…”
Section: Semantic Time Windows Based On Sequential Data Compressionmentioning
confidence: 99%