SAND in Action: Subsequence Anomaly Detection for Streams

Summary: Online, domain-agnostic subsequence anomaly detection for streams. SAND incrementally updates a drift-adaptive model, discards obsolete data, and avoids full-data access, enabling real-time subsequence anomaly detection and robust performance against competing streaming methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h80885251fb6fe140
Venue: VLDB
Year: 2021
Pagerank: -
Overall Rank: 13,782 | 7.37%
DOI: 10.14778/3476311.3476365
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Paul Boniol (University of Paris; Électricité de France)
2. John Paparrizos (University of Chicago)
3. Themis Palpanas (Institut universitaire de France; University of Paris)
4. Michael J. Franklin (University of Chicago)

BibTeX Citation

@article{boniol_vldb21,
        title = {{SAND in Action: Subsequence Anomaly Detection for Streams}},
        author = {Boniol, Paul and Paparrizos, John and Palpanas, Themis and Franklin, Michael J.},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {12},
        pages = {2867--2870},
        doi = {10.14778/3476311.3476365},
        url = {https://doi.org/10.14778/3476311.3476365},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 16 of 16 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,938	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.334286e-05
3,299	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	7.441222e-05
3,728	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0667899e-05
7,096	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.5991152e-05
7,647	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	5.4746904e-05
9,494	Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression	2025	VLDB	5.168414e-05
9,499	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.168414e-05
9,633	Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection	2022	VLDB	5.1459383e-05
9,665	Odyssey: An Engine Enabling The Time-Series Clustering Journey	2023	VLDB	5.142891e-05
9,916	SPARTAN: Data-Adaptive Symbolic Time-Series Approximation	2025	SIGMOD	5.1079647e-05
10,480	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	4.9769913e-05
10,494	MUFASA: Fast and Accurate Multivariate Time-Series Clustering	2026	SIGMOD	4.9769913e-05
10,521	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	4.9769913e-05
11,220	Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data	2025	SIGMOD	4.9769913e-05
11,355	BURST: Rendering Clustering Techniques Suitable for Evolving Streams	2025	VLDB	4.9769913e-05
11,755	Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances	2023	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 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	0.00010182038
1,629	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	0.0001003165
2,360	Online Outlier Detection in Sensor Data Using Non-Parametric Models	2006	VLDB	8.5774153e-05
2,376	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.5517908e-05
4,317	GRAIL: Efficient Time-Series Representation Learning	2019	VLDB	6.6651881e-05
4,705	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	6.4591377e-05
6,192	VergeDB: A Database for IoT Analytics on Edge Devices	2021	CIDR	5.8540507e-05

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