SDEcho: Efficient Explanation of Aggregated Sequence Difference
Summary: SDEcho automates finding causes of differences between SQL-derived aggregated sequences, emphasizing sequence-level patterns, order, and dimensional contributors. It uses hybrid pruning across pattern, order, and dimension (and their interactions) to compactly prune the explanation space for accurate, scalable explanations. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Fei Ye (Fudan University)
- 2. Zikang Liu (Fudan University)
- 3. Xi Zhang (Fudan University)
- 4. Yinan Jing (Fudan University)
- 5. Zhenying He (Fudan University)
- 6. Yuxin Che (Fudan University)
- 7. Haoran Xiong (Fudan University)
- 8. Kai Zhang (Fudan University)
- 9. X. Sean Wang (Fudan University)
BibTeX Citation
@article{ye_vldb25,
title = {{SDEcho: Efficient Explanation of Aggregated Sequence Difference}},
author = {Ye, Fei and Liu, Zikang and Zhang, Xi and Jing, Yinan and He, Zhenying and Che, Yuxin and Xiong, Haoran and Zhang, Kai and Wang, X. Sean},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {784--797},
doi = {10.14778/3712221.3712242},
url = {https://doi.org/10.14778/3712221.3712242},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,097 | Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V | 2023 | VLDB |
| 2 | 11,568 | SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads | 2024 | VLDB |
| 3 | 2,160 | DIFF: A Relational Interface for Large-Scale Data Explanation | 2019 | VLDB |
| 4 | 670 | A Formal Approach to Finding Explanations for Database Queries | 2014 | SIGMOD |
| 5 | 11,529 | Unstructured Data Fusion for Schema and Data Extraction | 2024 | SIGMOD |
| 6 | 9,550 | SQuID: Semantic Similarity-Aware Query Intent Discovery | 2018 | SIGMOD |
| 7 | 2,222 | Explaining Query Answers with Explanation-Ready Databases | 2016 | VLDB |
| 8 | 11,898 | Automated Relational Data Explanation using External Semantic Knowledge | 2022 | VLDB |
| 9 | 8,962 | GEqO: ML-Accelerated Semantic Equivalence Detection | 2023 | SIGMOD |
| 10 | 6,600 | Explain3D: Explaining Disagreements in Disjoint Datasets | 2019 | VLDB |