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Springer

26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022) (PAKDD)

Location: Chengdu , China

Conference dates: 5/16/2022 - 5/19/2022

Research H-index
16

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 170 112 117 16

Call for Papers

Research 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PAKDD2022 welcomes high-quality, original, and previously unpublished submissions in the theories, technologies and applications on all aspects of knowledge discovery and data mining.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.

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.
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 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
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
Submission Site: https://cmt3.research.microsoft.com/PAKDD2022

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

Overview

This comprehensive ranking presents a curated list of scientific conferences within the field of Computer Science, meticulously evaluated and assembled by Research.com. Recognized since 2014 as one of the leading platforms for science research across all major disciplines, Research.com is dedicated to delivering trusted and authoritative data on scientific contributions.

The position of each conference in this ranking is determined according to a unique bibliometric score developed by Research.com. This score integrates both the estimated h-index and the number of leading scientists who have participated or presented at the conference within the last three years, offering a multifaceted assessment of conference influence and prominence.

To ensure the reliability and depth of this ranking, the evaluation process encompassed an extensive review of over 2,742 conferences. These were selected following a rigorous inspection and critical analysis of more than 148,739 scientific documents authored by 13,184 distinguished and reputable Computer Science experts over the last three years. The collection and analysis of Impact Score values were completed as of 2024-11-27.

For those seeking a deeper understanding of the methodologies and bibliometric techniques applied in forming these rankings, detailed information is available on our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at Pacific-Asia Conference on Knowledge Discovery and Data Mining (based on the number of publications) are:

  • Svetha Venkatesh (16 papers) absent at the last edition,
  • Christos Faloutsos (15 papers) published 1 paper at the last edition the same number as at the previous edition,
  • Dinh Phung (15 papers) absent at the last edition,
  • Peter Christen (14 papers) published 1 paper at the last edition, 1 less than at the previous edition,
  • Philip S. Yu (11 papers) published 1 paper at the last edition, 1 less than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at Pacific-Asia Conference on Knowledge Discovery and Data Mining (based on the number of publications) are:

  • Chinese Academy of Sciences (40 papers) published 17 papers at the last edition, 15 more than at the previous edition,
  • Tsinghua University (30 papers) published 8 papers at the last edition, 5 more than at the previous edition,
  • Nanjing University (27 papers) published 10 papers at the last edition, 7 more than at the previous edition,
  • University of Technology, Sydney (27 papers) published 7 papers at the last edition, 4 more than at the previous edition,
  • Deakin University (21 papers) published 1 paper at the last edition, 2 less than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2019 edition, 1.20% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.73% were posted by at least one author from the top 10 institutions publishing at the conference. Another 7.27% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.82% of all publications and 38.18% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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