Sintel: A Machine Learning Framework to Extract Insights from Signals
Summary: Sintel: an end-to-end ML framework for time-series anomaly detection, unifying analysis, comparison, and logging. Human-in-the-loop annotations refine the pipeline, and the toolkit ships open code, data, and a spacecraft anomaly use case. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sarah Alnegheimish (Massachusetts Institute of Technology)
- 2. Dongyu Liu (Massachusetts Institute of Technology)
- 3. Carles Sala (Massachusetts Institute of Technology)
- 4. Laure Berti-Equille (Institut de Recherche pour le Développement)
- 5. Kalyan Veeramachaneni (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{alnegheimish_sigmod22,
title = {{Sintel: A Machine Learning Framework to Extract Insights from Signals}},
author = {Alnegheimish, Sarah and Liu, Dongyu and Sala, Carles and Berti-Equille, Laure and Veeramachaneni, Kalyan},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517910},
url = {https://dl.acm.org/doi/10.1145/3514221.3517910},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,420 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 6.2246363e-05 |
| 9,379 | Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains | 2025 | VLDB | 5.2755515e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,534 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00010464308 |
| 3,869 | Smile: A System to Support Machine Learning on EEG Data at Scale | 2019 | VLDB | 7.0609879e-05 |
| 7,473 | The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development | 2020 | SIGMOD | 5.609366e-05 |
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