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Workshop on Discovering Drift Phenomena in Evolving Data Landscape (DELTA)

Published: 24 August 2024 Publication History

Abstract

Automated systems must adapt to evolving environments, yet many struggle with drift phenomena affecting healthcare, finance, and cybersecurity domains. The DELTA workshop addresses this by distinguishing between data and concept drift, aiming to create a practical, human-centric framework for managing drift. The workshop seeks innovative drift detection, prediction, and analysis solutions by uniting researchers and practitioners. DELTA fosters collaboration to advance the understanding and management of drift in dynamic data landscapes by featuring keynotes, paper presentations, interactive sessions, and discussions.

References

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Oliver Cobb and Arnaud Van Looveren. 2022. Context-aware drift detection. In International Conference on Machine Learning. PMLR, 4087--4111.
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Flavio Corradini, Caterina Luciani, Andrea Morichetta, and Marco Piangerelli. 2023. Managing Variability of Large Public Administration Event Log Collections: Dealing with Concept Drift. In International Conference on Business Informatics Research. Springer, 31--44.
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Ege Berkay Gulcan and Fazli Can. 2023. Unsupervised concept drift detection for multi-label data streams. Artificial Intelligence Review, Vol. 56, 3 (2023), 2401--2434.
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Fabian Hinder, Valerie Vaquet, and Barbara Hammer. [n.,d.]. One or Two Things We Know about Concept Drift--A Survey on Monitoring in Evolving Environments. Part A: Detecting Concept Drift. Frontiers in Artificial Intelligence, Vol. 7 ( [n.,d.]), 1330257.
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Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang. 2018. Learning under concept drift: A review. IEEE transactions on knowledge and data engineering, Vol. 31, 12 (2018), 2346--2363.
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Bardh Prenkaj and Paola Velardi. 2023. Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis. IEEE Transactions on Knowledge and Data Engineering (2023).
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Bardh Prenkaj, Mario Villaizan-Vallelado, Tobias Leemann, and Gjergji Kasneci. 2023. Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes. arXiv preprint arXiv:2308.02353 (2023).
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Ylenia Rotalinti, Allan Tucker, Michael Lonergan, Puja Myles, and Richard Branson. 2022. Detecting drift in healthcare AI models based on data availability. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 243--258.
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Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, and Edson Emilio Scalabrin. 2021. A survey on concept drift in process mining. ACM Computing Surveys (CSUR), Vol. 54, 9 (2021), 1--38.
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Diego Stucchi, Luca Frittoli, and Giacomo Boracchi. 2022. Class distribution monitoring for concept drift detection. In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.

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cover image ACM Conferences
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2024

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Author Tags

  1. concept drift
  2. data drift
  3. drift explanation
  4. human-in-the-loop learning
  5. incremental learning

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  • CDC/HHS
  • European Union - NextGenerationEU under the Italian Ministry of University and Re- search (MUR) National Innovation Ecosystem

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KDD '24
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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