[dbjapan] 【12月7日へ締切延長】国際会議PAKDD2023

日本データベース学会の皆様 （重複してお受け取りになった場合はご容赦ください） 同志社大学の桂井です。お世話になっております。 来年5月25日〜28日に大阪で開催されるPAKDD2023に関しまして、投稿締切が*** *2022年12月7日（水）**** に延長されました。皆様からのご投稿をお待ちしております。 論文投稿に加え、*ワークショップ*のご提案もお待ちしております。 機械学習・データマイニング・データサイエンスの基礎や応用、学際的な話題など幅広いトピックを歓迎いたします。プログラムには招待講演などを含めることも可能です。 https://pakdd2023.org/cfw/ ワークショップの提案締切は2022年12月1日（木）となっています。 どうぞよろしくお願いいたします。 ======================================================== [Final Call for Papers] The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023) May 25-28, 2023 - Osaka, Japan (Onsite/online hybrid) http://pakdd2023.org/ [Important Dates] Paper Submission Deadline: Dec. 7, 2022 Paper Acceptance Notification: Feb. 7, 2023 Camera Ready Papers Due: Mar. 10, 2023 *All deadlines are 23:59 Pacific Standard Time (PST) [Paper submission] https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FPAKDD2023 =============================================================== 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. The 27th edition of PAKDD will be held in Osaka, Japan, from May 25 to May 28, 2023. The venue will be a hybrid of onsite and online. [Topics] PAKDD2023 welcomes high-quality, original, and previously unpublished submissions in the theories, technologies, and applications on all aspects of knowledge discovery and data mining. Topics of relevance for the conference include, but not limited to the following. Methods and algorithms: Anomaly and outlier detection, Association rule, Classification, Clustering, Data mining pipelines, Deep learning, Dimensionality detection and feature selection, Ethics and fairness, Graphs and networks, Interpretability and explainability, Kernel methods, Matrices and tensors, Online and streaming algorithms, Parallel and distributed mining, Probabilistic models and statistical inference, Regression, Reinforcement learning, Relational learning, Security and privacy, Semi-supervised and unsupervised learning, Theoretical foundations, Transfer learning and meta learning, and Visualization and user interface. Applications: Big data, Computational Advertising, Financial data, Information retrieval and search, Internet of Things, Intrusion and fraud detection, Medical and biological data, Multimedia and multimodal data, Recommender systems, Robotics, Scientific data, Social network analysis, Spatio-temporal data, Texts, web, social media, and Time-series and streaming data. [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 must 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 must use Springer LNCS/LNAI manuscript submission guidelines for their submissions. All papers must be submitted electronically through the CMT paper submission system in PDF format only. 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. We require that any submission to PAKDD must not be already published or under review at another archival conference or journal. Papers on arXiv do not violate this rule as long as the submitted paper does not cite them. 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. 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. [Double-Blind Review] Paper submission must adhere to the double-blind review policy. Submissions must remove all details identifying the author(s) 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 PAKDD2023. An exception to this rule applies to manuscripts that were published in arXiv not later than October 24, 2022, i.e., at least a month before PAKDD’s submission deadline. The author list and order cannot be changed after the paper is submitted. [Formatting Template] Formatting Template: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu… . All the Manuscripts must be prepared and submitted in accordance with the above format. Usage of other formats may lead to disqualification of paper for the conference. [Submission Site] https://cmt3.research.microsoft.com/PAKDD2023 [Contact Information] Program Co-Chairs of PAKDD2023 Hisashi Kashima, Wen-Chih Peng, Tsuyoshi Ide pakdd2023(a)gmail.com -- ************************************* Marie Katsurai, PhD Associate Professor Department of Intelligent Information Engineering and Sciences Doshisha University E-mail: katsurai(a)mm.doshisha.ac.jp Website: https://mm.doshisha.ac.jp/