KDD: Knowledge Discovery and Data Mining



Past:   Proceedings on DBLP

Future:  Post a CFP for 2024 or later   |   Invite the Organizers Email


All CFPs on WikiCFP

Event When Where Deadline
Aug 6, 2023 - Aug 10, 2023 Long Beach,CA Feb 2, 2023
KDD 2022 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 14, 2022 - Aug 18, 2022 Washington DC Feb 10, 2022
KDD 2021 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 14, 2021 - Aug 18, 2021 Singapore Feb 8, 2021
KDD 2020 KDD 2020
Aug 22, 2020 - Aug 27, 2020 San Diego, California - USA Feb 13, 2020
Aug 3, 2019 - Aug 7, 2019 Anchorage, Alaska - USA Feb 3, 2019
KDD 2018 Knowledge Discovery and Data Mining Conference
Aug 19, 2018 - Aug 23, 2018 London, UK Feb 11, 2018
KDD Cup 2017 Call for Proposals
Aug 13, 2017 - Aug 17, 2017 Halifax, Nova Scotia, Canada Dec 9, 2016
KDD 2016 22nd ACM SIGKDD international conference on knowledge discovery and data mining
Aug 13, 2016 - Aug 17, 2016 San Francisco, USA Feb 12, 2016
KDD 2015 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 10, 2015 - Aug 13, 2015 Sydney, Australia Feb 20, 2015
KDD 2014 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 24, 2014 - Aug 27, 2014 New York, USA Feb 21, 2014 (Feb 13, 2014)
KDD 2013 ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 11, 2013 - Aug 14, 2013 Chicago, USA Feb 22, 2013 (Feb 15, 2013)
KDD 2012 18th ACM SIGKDD Knowledge Discovery and Data Mining
Aug 12, 2012 - Aug 16, 2012 Beijing, China Feb 10, 2012
KDD 2011 The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 21, 2011 - Aug 24, 2011 San Diego, California Feb 18, 2011 (Feb 11, 2011)
KDD 2010 The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Jul 25, 2010 - Jul 28, 2010 Washington DC, USA Feb 5, 2010 (Feb 2, 2010)
KDD 2009 The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Jun 28, 2009 - Jul 1, 2009 Paris, France Feb 6, 2009 (Feb 2, 2009)
KDD 2008 The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Aug 24, 2008 - Aug 27, 2008 Las Vegas, USA Feb 29, 2008 (Feb 22, 2008)

Present CFP : 2023

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KDD 2023
Call for Research Track Papers
Call for Applied Data Science (ADS) Track Papers

Call for Research Track Papers
Important Dates
Paper Submission: Feb 2, 2023
Author/Reviewer Interaction and Reviewer Discussion: April 6-27, 2023
Final Notification: May 18, 2023
Camera-ready: June 10, 2023
Conference: August 6-10, 2023
All deadlines are at 11:59 PM anytime in the world.
Website for submissions: TBD (The submission site will be open on Jan 19, 2023).

Submission and Formatting Instructions
KDD is a dual track conference hosting both a Research and an Applied Data Science Track. A paper should either be submitted to the Research Track or the Applied Data Science Track but not both. Research Track submissions are limited to 9 pages, excluding references, must be in PDF and use ACM Conference Proceeding templates (two column format). Submissions to the Research Track are double blind (no author names should be listed). The recommended setting for Latex file of anonymous manuscript is:
\documentclass[sigconf, anonymous, review]{acmart}.
Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit. As in previous years, the supplement should be included in the same file with the main manuscript. The paper should be self-contained, since reviewers are not required to read the supplement. Note that the supplement will not be included in the proceedings.

The Word template guideline can be found here: https://www.acm.org/publications/proceedings-template
The Latex/overleaf template guideline can be found here: https://www.overleaf.com/latex/templates/association-for-computing-machinery-acm-sig-proceedings-template/bmvfhcdnxfty

Research Track Aim and Scope
KDD is the premier Data Science conference. We invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Topics of interest include, but are not limited to:

Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, sampling, parallel and distributed data science (cloud, map-reduce, federated learning), novel algorithmic and statistical techniques for big data, data cleaning and preparation that uses learning, algorithmically efficient data transformation and integration.
Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, transfer learning, representation learning, meta learning, reinforcement learning; classification, clustering, regression, semi-supervised, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.
Important policies
Authors are strongly recommended to review the following instructions.

PC Member/Reviewer Application
Those who are interested in serving as a Research Track PC Member/Reviewer are welcome, to that end, please fill an application using this form. The PC Co-chairs will review the application.

Review Contribution
All authors will be required to register as reviewers for KDD. Not all authors will be requested to provide reviews, but if an author is requested to provide up to 3 timely reviews for KDD and declines to do so when requested, their submission will be rejected.

Blinded Review and No Concurrent Submissions
Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication in an archival venue. Papers submitted to the KDD Research Track follow a double-blind review process. If a previous version of the paper was submitted to a non-archival venue, such as a workshop or to arXiv, the title and abstract must be changed in the KDD submission. KDD submissions must not be in concurrent submission to any archival conference or journal during the KDD review period. Papers that appear in arXiv after Jan 2th, 2023 until the end of the review process will not be accepted.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available after the review process. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes model parameters, experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission. Papers that do not have a clear reason for using publicly available data (such as the use of confidential patient data) should aim to provide simulated data that has the same properties as the dataset they are studying, and/or find publicly available datasets to test their approach.

Every person named as the author of a paper must have contributed substantially to the work described in the paper and/or to the writing of the paper. Every listed author must take responsibility for the entire content of a paper. Persons who do not meet these requirements may be acknowledged, but should not be listed as authors. Post-submission changes to the set of authors list are not allowed. Authorship may not be modified after the paper submission deadline.

Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library. The rights retained by authors who transfer copyright to ACM can be found here.

Official Publication Date
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date for KDD 2023 is on or after July 15, 2023. The official publication date affects the deadline for any patent filings related to published work.

By submitting paper(s) to KDD 2023, the authors agree that the reviews and discussions may be made public for all accepted papers.

At least one author of each accepted paper must register for KDD to present their work in person.

Email: KDD23-pc-chairs@acm.org
Leman Akoglu (Carnegie Mellon University)
Dimitrios Gunopulos (National and Kapodistrian University of Athens)
Xifeng Yan (University of California at Santa Barbara)

Research Track PC Co-chairs of KDD-2023

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