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DELTA 2024 : Workshop on Discovering Drift Phenomena in Evolving Landscape


When Aug 26, 2024 - Aug 26, 2024
Where Barcelona
Abstract Registration Due May 21, 2024
Submission Deadline May 28, 2024
Notification Due Jun 28, 2024
Final Version Due Jul 2, 2024
Categories    data/concept/hybrid drift   adaptive systems   data streaming   evolving systems

Call For Papers

Call For Papers
Workshop on Discovering Drift Phenomena in Evolving Landscape (DELTA 2024)
to be held as part of the ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD 2024)
Workshop adjunct proceedings published by Springer-Verlag:
Date: August 25th, 2024 - Barcelona, Catalonia (Spain)
Important Dates
Abstract Submissions: May 21st, 2024
Paper Submissions: May 28th, 2024
Notifications: June 28th, 2024
Camera-Ready Contributions: July 2nd, 2024
Workshop: August 26th,2024
All deadlines are 11:59 pm Pacific Time
Workshop Aims and Scope
In today’s rapidly evolving landscape, integrating automated systems into
daily tasks are the primary objectives for both industry and
academia. However, a challenge arises as these systems often struggle to adapt
to the continuous evolution of the world. Despite recent attention on the term
‘distribution drift’ to describe this continuous evolution. This overarching term conflates
data drift and concept drift lead to misunderstanding in research and practitioner communities.
To bridge the gap, the DELTA workshop fosters collaboration
between academia and industry to clarify evolving landscape challenges and
develop practical solutions. We invite authors to submit unpublished,
original papers addressing the complexities faced by current state-of-the-art
techniques in detecting, predicting, and analyzing drift phenomena in real-world domains.
Workshop Keywords
Concept drift · Data drift · Hybrid drift · Data Streaming · Adaptive Systems · Evolving Systems
Workshop Topics
DELTA welcomes research and perspective contributions on all topics related to
drift in evolving landscapes across domains (e.g., finance, business, basic
sciences, construction computational advertising, IoT, etc.) and independent of
data types (e.g., networks, tabular, unstructured, graphs, logs, spatiotemporal,
multimedia, time series, genomic sequences, and streaming data.):
Online and Incremental Learning
(Self-)Adaptive Systems
Human-in-the-Loop Learning
Uncertainty Quantification for Drift Learning
Drift Detection, Prediction, and Analysis
Process Mining for evolving environments
Process Drift Analysis
Drift Explanation
Submission and Publication
All submissions must be written in English and submitted electronically in a PDF format,
through the CMT submission system:
Submissions must follow the guidelines of the Communications in Computer and Information Science (CCIS) series by Springer-Verlag here found here : The submission must also be anonymized; authors must omit their names and affiliations from submissions and avoid obvious identifying statements (e.g., citations to the author’s own prior work should be made in the third person). Finally, the submission must not be currently under review at another publication venue. Failure to adhere to policies will result in desk rejection.

We strongly recommend using the LaTeX CCIS template and providing paper code and data in an Anonymous GitHub repository (
We encourage three types of submissions (reviewers will comment on whether the size is appropriate for each contribution):
Full papers (up to 12 pages) should concern the state of the art and state the proposal's contribution in the application domain, even if they present preliminary results.
In particular, research papers should describe the methodology in detail, experiments should be repeatable, and approaches should be compared with those in the literature.
Reproducibility/Replicability papers (up to 12 pages) should repeat prior experiments using the original source code and datasets to show how, why, and when the methods work or not (replicability papers) or should repeat prior experiments, preferably using the original source code in new contexts (e.g., different domains and datasets, different evaluation and metrics) to generalize further and validate or not previous work (reproducibility papers).
Short or position papers (up to 6 pages) should introduce new points of view on the workshop topics or summarize a group's experience in the field. Practice and experience reports must detail real-world scenarios in which drifts are managed.
Registration and Presentation Policy
The workshop will be held in person. Each accepted workshop paper must be accompanied
by at least one distinct full author registration, completed by the early registration date cut-off.
Each accepted workshop paper must be presented in person.
The Main Conference organization team will manage the registration:
Workshop Chairs
Marco Piangerelli
University of Camerino, Camerino (Italy)
Bardh Prenkaj
Sapienza University of Rome (Italy)
Ylenia Rotalinti
Brunel University London, London (UK)
Ananya Joshi
Carnegie Mellon University, Pittsburgh (USA)
Giovanni Stilo
University of L’Aquila, L’Aquila (Italy)
For general inquiries about the workshop, please email,,,,

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