Original title: Compressor-Aware Feature Analysis and Learning-Based Performance Prediction for Scientific Data Compression
Qirui Tian, Zhenlu Qin, Lei Wu, Jinzhen Wang · September 2026
Modern scientific instruments and computer simulations create huge amounts of data very quickly. This data flood overwhelms computer storage, networks, and analysis tools. To fix this, scientists compress the data, meaning they shrink file sizes by removing tiny bits of precision that do not hurt scientific accuracy. However, predicting how small files will get and how fast compression will run is hard. Researchers studied the inner mechanics of a popular compressor called SZ. They picked out key internal features and built machine learning models to forecast compression speed and file shrinking ratios. They also built a simplified version of this model. They found that looking at the compressor's inner workings is vital for accurate forecasts, though the ideal tool depends on the situation.
You are packing a suitcase. You know the clothing items, but the space saved by folding a sweater depends on the folding technique. The folding technique itself changes based on what else is already packed. In this paper, the compression performance of a scientific data compressor depends on its internal behaviors, which vary with the data.
Scientists and engineers handling massive datasets from simulations or instruments rely on compression to save storage and speed up transmission. But compression performance is notoriously unpredictable. This work reveals that predicting it requires understanding the compressor's internal behavior, not just the data — enabling smarter decisions about when and how to compress.
The study only analyzes SZ, a specific lossy compressor, so findings may not transfer to other compressors. The scenario-dependent performance means no universal best method is identified. The abstract does not provide quantitative accuracy results or comparison with non-prediction baselines.
Ask anything about this paper. Answers come from its abstract, and say so when the abstract doesn't cover your question.
Compressor-Aware Feature Analysis and Learning-Based Performance Prediction for Scientific Data Compression — Qirui Tian, Zhenlu Qin, Lei Wu et al. — Journal of Artificial Intelligence for Automation — doi:10.53941/jaia.2026.100013
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.