Conference Information
KDD 2024: ACM SIGKDD Conference on Knowledge Discovery and Data Mining
https://kdd2024.kdd.org/
Submission Date:
2024-08-01
Notification Date:
Conference Date:
2024-08-25
Location:
Barcelona, Spain
Years:
30
CCF: a   CORE: a*   QUALIS: a1   Viewed: 224295   Tracked: 518   Attend: 55

Call For Papers
Scope

For the research track, 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 social networks, time series, sequences, streams, text, web, graphs, rules, patterns, logs, IoT data, spatio-temporal data, biological data, scientific and business data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
    Big Data: Large-scale systems for data analysis, machine learning, optimization, sampling, summarization; parallel and distributed data science (cloud, map-reduce, federated learning); novel algorithmic and statistical techniques for big data; 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 learning, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.
Last updated by Dou Sun in 2024-03-24
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
201898310710.9%
2017748648.6%
20161115665.9%
201581916019.5%
2014103615114.6%
201372612517.2%
201275513317.6%
201171412617.6%
201057810117.5%
200953710519.6%
200859311819.9%
200757311119.4%
20064575010.9%
20053587621.2%
20043374011.9%
20032583413.2%
20012035225.6%
20002465020.3%
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Related Journals
CCFFull NameImpact FactorPublisherISSN
aIEEE Transactions on Knowledge and Data Engineering3.857IEEE1041-4347
bData Mining and Knowledge Discovery3.670Springer1384-5810
bData & Knowledge Engineering1.992Elsevier0169-023X
Statistical Analysis and Data Mining John Wiley & Sons, Ltd1932-1872
Knowledge Engineering ReviewCambridge University Press0269-8889
BioData Mining2.522Springer1756-0381
cInternational Journal of Software Engineering and Knowledge Engineering World Scientific0218-1940
bKnowledge and Information Systems2.822Springer0219-1377
cInternational Journal of Knowledge Management IGI Global1548-0666
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