Paul Boniol

Orcid: 0000-0001-8516-0123

According to our database1, Paul Boniol authored at least 23 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Time-Series Anomaly Detection: Overview and New Trends.
Proc. VLDB Endow., August, 2024

Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination.
Image Process. Line, 2024

dsymb Playground: An Interactive Tool to Explore Large Multivariate Time Series Datasets.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

ADecimo: Model Selection for Time Series Anomaly Detection.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

An Interactive Dive into Time-Series Anomaly Detection.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

2023
Correction to: Unsupervised and scalable subsequence anomaly detection in large data series.
VLDB J., March, 2023

Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series.
Proc. VLDB Endow., 2023

Appliance Detection Using Very Low-Frequency Smart Meter Time Series.
Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

New Trends in Time Series Anomaly Detection.
Proceedings of the Proceedings 26th International Conference on Extending Database Technology, 2023

2022
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection.
Proc. VLDB Endow., 2022

Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection.
Proc. VLDB Endow., 2022

Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection.
Proc. VLDB Endow., 2022

dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification.
Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

2021
Detection of anomalies and identification of their precursors in large data series collections. (Détection d'anomalies et identification de leurs précurseurs dans des grandes collections de séries temporelles).
PhD thesis, 2021

Unsupervised and scalable subsequence anomaly detection in large data series.
VLDB J., 2021

SAND in Action: Subsequence Anomaly Detection for Streams.
Proc. VLDB Endow., 2021

SAND: Streaming Subsequence Anomaly Detection.
Proc. VLDB Endow., 2021

2020
GraphAn: Graph-based Subsequence Anomaly Detection.
Proc. VLDB Endow., 2020

Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series.
Proc. VLDB Endow., 2020

Unsupervised Subsequence Anomaly Detection in Large Sequences.
Proceedings of the VLDB 2020 PhD Workshop co-located with the 46th International Conference on Very Large Databases (VLDB 2020), ONLINE, August 31, 2020

Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France.
Proceedings of the Natural Legal Language Processing Workshop 2020 co-located with the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020), 2020

Automated Anomaly Detection in Large Sequences.
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

SAD: An Unsupervised System for Subsequence Anomaly Detection.
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020


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