SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis
Summary: SAIL: a modular web engine enabling the largest empirical study of symbolic approximation (7 methods, 100+ datasets) with interactive exploration. Finds SPARTAN (intrinsic-dim modeling + dynamic per-segment alphabets) consistently outperforms rivals across tasks without extra storage/runtime; SAX remains a strong budget baseline, with SFA the only method to beat SAX at equal storage. (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. Fan Yang (Ohio State University)
- 2. John Paparrizos (Ohio State University)
BibTeX Citation
@article{yang_vldb25,
title = {{SAIL: A Voyage to Symbolic Approximation Solutions for Time-Series Analysis}},
author = {Yang, Fan and Paparrizos, John},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5419--5422},
doi = {10.14778/3750601.3750686},
url = {https://doi.org/10.14778/3750601.3750686},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,256 | HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series | 2026 | SIGMOD | 5.093636e-05 |
| 10,271 | MUFASA: Fast and Accurate Multivariate Time-Series Clustering | 2026 | SIGMOD | 5.093636e-05 |
| 10,298 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,579 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00010305183 |
| 2,004 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3207067e-05 |
| 9,733 | SPARTAN: Data-Adaptive Symbolic Time-Series Approximation | 2025 | SIGMOD | 5.227679e-05 |
| 10,747 | A Structured Study of Multivariate Time-Series Distance Measures | 2025 | SIGMOD | 5.093636e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,382 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB |
| 2 | 7,309 | Set-based Similarity Search for Time Series | 2016 | SIGMOD |
| 3 | 9,477 | Odyssey: An Engine Enabling The Time-Series Clustering Journey | 2023 | VLDB |
| 4 | 8,229 | Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data | 2007 | VLDB |
| 5 | 9,698 | TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis | 2024 | VLDB |
| 6 | 898 | Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures | 2008 | VLDB |
| 7 | 11,107 | Representative Time Series Discovery for Data Exploration | 2025 | VLDB |
| 8 | 4,234 | GRAIL: Efficient Time-Series Representation Learning | 2019 | VLDB |
| 9 | 190 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD |
| 10 | 9,733 | SPARTAN: Data-Adaptive Symbolic Time-Series Approximation | 2025 | SIGMOD |