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)

Paper ID: 14171
Venue: VLDB
Year: 2025
Pagerank: -
Overall Rank: 13,137 | 8.61%
DOI: 10.14778/3750601.3750686

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Authors

1. Fan Yang
2. John Paparrizos

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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
1,516	k-Shape: Efficient and Accurate Clustering of Time Series	2015	SIGMOD	0.00011586255
2,381	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	8.9327638e-05
9,599	SPARTAN: Data-Adaptive Symbolic Time-Series Approximation	2025	SIGMOD	4.3177432e-05
10,466	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	4.1945683e-05

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