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PAKDD 2022 : The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022)

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Conference Series : Pacific-Asia Conference on Knowledge Discovery and Data Mining
 
Link: http://www.pakdd.net/
 
When May 16, 2022 - May 19, 2022
Where Chengdu, China
Submission Deadline Nov 15, 2021
Categories    knowledge management   data mining   data management   knowledge discovery
 

Call For Papers

★Full name: The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022)
★Abbreviation: PAKDD 2022
★CCF Recommended Conferences (C series)

★Conference Date
--Start Date: May 16, 2022
--End Date: May 19, 2022

★Conference Location
--Country: China
--City: Chengdu, Sichuan

★Website: http://www.pakdd.net/
★Submission Link: https://cmt3.research.microsoft.com/PAKDD2022

★About PAKDD2022
Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. The PAKDD 2022 is in the list of CCF Recommended Conferences (C series) and 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.

★Organized by
Southwest Jiaotong University (SWJTU)

★Honorary Co-Chairs
Dan Yang (Southwest Jiaotong University, China)
Zhi-Hua Zhou (Nanjing University, China)

★General Co-Chairs
Enhong Chen (University of Science and Technology of China, China)
Yu Zheng (JD.com, China)

★Program Committee Co-Chairs
Joao Gama (University of Porto, Portugal)
Tianrui Li (Southwest Jiaotong University, China)
Yang Yu (Nanjing University, China)

★Workshop Co-Chairs
Gill Dobbie (University of Auckland, New Zealand)
Can Wang (Griffith University, Australia)

★Tutorial Co-Chairs
Gang Li (Deakin University, Australia)
Tanmoy Chakraborty (Indraprastha Institute of Information Technology Delhi, India)

★Local Arrangement Co-Chairs
Yan Yang (Southwest Jiaotong University, China)
Chuan Luo (Sichuan University, China)
Xin Yang (Southwestern University of Finance and Economics, China)

★Sponsor Co-Chairs
Xiaobo Zhang (Southwest Jiaotong University, China)

★Publicity Co-Chairs
Xiangnan Ren (Group 42, United Arab Emirates)
Hao Wang (Zhejiang Lab, China)
Junbo Zhang (JD.com, China)
Chongshou Li (Southwest Jiaotong University, China)

★Proceedings Chair
Fei Teng (Southwest Jiaotong University, China)

★Web and Content Co-Chairs
Xiaole Zhao (Southwest Jiaotong University, China)
Zhen Jia (Southwest Jiaotong University, China)

★Registration Chairs
Hongmei Chen (Southwest Jiaotong University, China)
Jie Hu (Southwest Jiaotong University, China)
Yanyong Huang (Southwestern University of Finance and Economics, China)

★Topics
PAKDD2022 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:
* 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 Technologies
--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.

★Conference Proceedings
--Springer will publish the proceedings of the conference as a volume of the LNAI series.

★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.
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.

★Awards
The conference will confer several awards, including Best Paper Award, Best Student Paper Award, and Best Application Paper Award from the submissions.
Those papers predominately dealing with computer vision core problems are less likely to be relevant to the conference. The reviewers will have less understanding of these problems to evaluate the papers.

★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 PAKDD2022. An exception to this rule applies to manuscripts that were published in arXiv not later than September 17, 2021, 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: http://www.springer.de/comp/lncs/authors.html.
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/PAKDD2022

★How to Submit: https://cmt3.research.microsoft.com/docs/help/author/author-submission-form.html


★Call for Workshops
PAKDD2022 seeks workshop proposals on foundational and emerging topics in areas related to data mining. The PAKDD workshops provide an informal and vibrant opportunity for researchers and industry practitioners to share their research positions, original research results and practical development experiences on specific challenges and emerging issues. Each workshop should be focused on a cohesive theme so that participants can benefit from interaction with each other.
A list of topics (non-exhaustive) includes:
* Foundational topics in data mining
* Big data mining/platform
* Data mining on specialized data types: graphs, structured/unstructured/semi-structured data, streaming data, time series, spatial-temporal data, text, multimedia, social networks, etc.
* Data mining in specific disciplines or interdisciplinary topics: biology, agriculture, natural resources (land, water, soil, plants and animals) management, education, open distance learning, ecology, e-government, environmental sciences, finance, healthcare, manufacturing, social sciences, etc.
* Data mining on cloud computing
* Data mining and privacy
* Data mining and security
* Data analysis and mining for new applications: smart devices, smart grids, smart homes, etc.
* Data analytical processing Deep learning models and applications
For more information: http://www.pakdd.net/workshop.html

★Call for Tutorials
We invite proposals for half-day (3-4 hours) or full-day (5-8 hours) tutorials from active researchers in both academia and industry who are experienced and engaging presenters.
Ideally, a tutorial will cover the state-of-the-art research, development, and applications in a specific data mining domain, to stimulate and facilitate future work. Tutorials on interdisciplinary areas, novel and fast growing directions, and significant applications are highly encouraged.
A tutorial proposal should include the following and should not exceed 5 pages excluding references:
--Title
--Abstract
--Tutorial Outline
--Presenters' name, affiliation, address, email, phone
--A biographical sketch of the presenter(s)
--References
Outline including a short summary of every section
--Background
--Specific goals and objectives
--Expected background of the audience
--Audio Visual equipment needed for the presentation
A list of up to 20 most important references that will be covered in the tutorial.
For more information: http://www.pakdd.net/cft.html

★Contact Us
PAKDD2022 Secretariat
Email: pakdd2022@gmail.com
Telephone: +86-87555888 / +86-13281280917

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