会议信息
PAKDD 2021: Pacific-Asia Conference on Knowledge Discovery and Data Mining
https://pakdd2021.org/
截稿日期:
2020-11-23
通知日期:
2021-02-01
会议日期:
2021-05-11
会议地点:
Delhi, India
届数:
25
CCF: c   CORE: a   浏览: 51755   关注: 221   参加: 41

会议地点
征稿
Conference Scope

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.

Topics

PAKDD 2021 welcomes high-quality, original, and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining. Topics of relevance for the conference include, but not limited to, the following:

    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, sampling, parallel and distributed data mining (cloud, map-reduce, federated learning), novel algorithmic, and statistical techniques for big data.
    Foundations:
    Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, meta-learning, reinforcement learning; classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, and robustness

Paper Submission

Paper submission must be in English. All papers will be double-blind reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Papers that do not comply with the Submission Policy will be rejected without review.

Each submitted paper should include an abstract up to 200 words and be no longer than 12 single-spaced pages with 10pt font size (including references, appendices, etc.). Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their submissions. All papers must be submitted electronically through the paper submission system in PDF format only. If required supplementary material may be submitted as a separate PDF file, but reviewers are not obligated to consider this, and your manuscript should, therefore, stand on its own merits without any supplementary material. Supplementary material will not be published in the proceedings.

The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. For no-show authors, their papers will not be included in the proceedings. Before submitting your paper, please carefully read and agree with the PAKDD Paper Submission Policy and No-Show Policy: https://pakdd.org/policies/.

The conference will confer several awards, including Best Paper Award, Best Student Paper Award, and Best Application Paper Award from the submissions.

Springer will publish the proceedings of the conference as a volume of the LNAI series, and selected excellent papers will be invited for publications in special issues of high-quality journals, including Knowledge and Information Systems (KAIS) and International Journal of Data Science and Analytics.

Double-Blind Review

Paper submission must adhere to the double-blind review policy. Submissions must have all details identifying the author(s) removed from the original manuscript (including the supplementary files, if any), and the author(s) should refer to their prior work in the third person and include all relevant citations.

Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for PAKDD 2021. An exception to this rule applies to manuscripts that were published in arXiv not later than 30th October 2020, i.e., at least a month before PAKDD’s submission deadline. These can be submitted to PAKDD provided that the submitted paper’s title and abstract are different from the one appearing on arXiv. Any submission shall not appear in arXiv until the review process has ended.

The author list and order cannot be changed after the paper is submitted.
最后更新 Dou Sun 在 2020-09-03
相关会议
CCFCOREQUALIS简称全称截稿日期通知日期会议日期
aa*a1KDDACM SIGKDD Conference on Knowledge Discovery and Data Mining2020-02-132020-05-152020-08-22
ICMAEInternational Conference on Mechanical and Aerospace Engineering2020-02-052020-02-252020-07-14
EIIS&T International Symposium on Electronic Imaging2019-09-30 2020-01-26
EMMCVPRInternational Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition2014-07-302014-09-142015-01-01
EBICCInternational Brazilian Meeting on Cognitive Science2015-09-302015-10-302015-12-07
IECCInternational Electronics Communication Conference2020-05-252020-06-102020-07-08
aa*a1IJCAIInternational Joint Conference on Artificial Intelligence2020-01-152020-04-192020-07-11
cb2DGCIInternational Conference on Discrete Geometry for Computer Imagery2012-09-072012-11-142013-03-20
CFCSNInternational Conference on Frontiers of Control and Sensor Networks2020-11-202020-11-302020-12-16
b1AICTAdvanced International Conference on Telecommunications2020-05-182020-07-182020-09-27
推荐