Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data
Summary: Presents a rigorous framework to enforce error bounds on univariate and multivariate QoIs during lossy compression, providing formal guarantees for downstream scientific analyses. Modularly integrates with compressors to preserve QoIs (kinetic energy, averages, isosurfaces) while achieving up to 4× higher compression than state-of-the-art. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Pu Jiao
- 2. Sheng Di
- 3. Hanqi Guo
- 4. Kai Zhao
- 5. Jiannan Tian
- 6. Dingwen Tao
- 7. Xin Liang
- 8. Franck Cappello
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,149 | Serf: Streaming Error-Bounded Floating-Point Compression | 2025 | SIGMOD | 4.3849295e-05 |
| 10,381 | LCP: Enhancing Scientific Data Management with Lossy Compression for Particles | 2025 | SIGMOD | 4.1945683e-05 |
| 10,614 | QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression | 2025 | VLDB | 4.1945683e-05 |
| 10,937 | High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation | 2024 | SIGMOD | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,100 | Query Optimization In Compressed Database Systems | 2001 | SIGMOD | 0.00014072277 |
| 4,531 | Efficient Document Analytics on Compressed Data: Method, Challenges, Algorithms, Insights | 2018 | VLDB | 6.1073703e-05 |
Previous
Page 1 / 1
Next