Clustering-based compression for raster time series
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Clustering-based compression for raster time seriesAuthor(s)
Date
2024-09-29Citation
Martita Muñoz, José Fuentes-Sepúlveda, Cecilia Hernández, Gonzalo Navarro, Diego Seco, Fernando Silva-Coira, Clustering-based compression for raster time series, The Computer Journal, 2024;, bxae090, https://doi.org/10.1093/comjnl/bxae090
Abstract
[Abstract]: A raster time series is a sequence of independent rasters arranged chronologically covering the same geographical area. These are commonly used to depict the temporal evolution of represented variables. The T-k2-raster is a compact data
structure that performs very well in practice for compact representations for raster time series. This structure classifies each raster as a snapshot or a log and encodes logs concerning their reference snapshots, which are the immediately preceding
selected snapshots. An enhanced version of the T-k2-raster, called Heuristic T-k2-raster, incorporates a heuristic for automating the selection of snapshots. In this study, we investigate the optimality of the heuristic employed in Heuristic T-k2- raster by comparing it with a dynamic programming approach. Our experimental evaluation demonstrates that Heuristic T-k2-raster is a near-optimal solution, achieving compression performance almost identical to the dynamic programming method. These results indicate that variations of the structure that maintain the temporal order of the rasters are unlikely to significantly improve compression. Consequently, we explore an alternative approach based on clustering, where rasters are grouped according to their similarity, regardless of their temporal order. Our experimental evaluation reveals that this clustering-based strategy can enhance compression in scenarios characterized by cyclic behavior.
Keywords
Raster dataset
Temporal Raster
Data compression
Compact Data Structure
Clustering
Dynamic Programming
Temporal Raster
Data compression
Compact Data Structure
Clustering
Dynamic Programming
Description
Real world datasets, and scripts to generate the synthetic and semi-synthetic datasets are available at https://figshare.com/s/5ad53959f8eed8a83f83.
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This is a pre-copyedited, author-produced version of an article accepted for publication in The Computer Journal, following peer review. The version of record [Martita Muñoz, José Fuentes-Sepúlveda, Cecilia Hernández, Gonzalo Navarro, Diego Seco, Fernando Silva-Coira, Clustering-based compression for raster time series, The Computer Journal, 2024;, bxae090,] is available online at: https://doi.org/10.1093/comjnl/bxae090
© 2024, OUP © The British Computer Society 2024.