posted by organizer: tigerzhang || 22367 views || tracked by 55 users: [display]

KDD 2017 : Call for Applied Data Science Papers


When Aug 13, 2017 - Aug 17, 2017
Where Halifax, Nova Scotia, Canada
Submission Deadline Feb 17, 2017
Notification Due May 19, 2017
Final Version Due Jun 9, 2017
Categories    knowledge discovery   data mining   data science   machine learning

Call For Papers

KDD 2017 Call for Applied Data Science Papers

Key dates
* Submission: February 17, 2017
* Notification: May 19, 2017
* Camera-ready: June 9, 2017
* Short Promotional Video (Required); June 9, 2017
* Source Code and Presentation (Optional): June 9, 2017
(All deadlines are at 11:59PM Alofi Time)

We invite submissions of papers describing designs and implementations of solutions and systems for practical tasks in data mining, data analytics, data science, and applied machine learning.

Application domains of interest include, but are not limited to, education, transportation, manufacturing, finance, retail, healthcare, e-commerce, telecommunications, law, public policy, government, and nonprofit settings. The primary emphasis is on papers that advance the understanding of, and show how to deal with, practical issues related to deploying data science technologies. This track also highlights new research challenges motivated by analytics and data science applications in the real world.

Submitted papers will go through a competitive peer review process. The Applied Data Science Track is distinct from the Research Track in that submissions solve real-world problems and focus on systems that are deployed or are in the process of being deployed. Submissions must clearly identify one of the following three areas they fall into: "deployed", "discovery", or "foundational". The criteria for submissions in each category are as follows:
* Deployed: Must describe implementation of a system that solves a significant real-world problem. The focus should be on describing the problem, its significance, decisions and tradeoffs made when making design choices for the solution, deployment challenges, and lessons learned.
* Discovery: Must include results that are findings with demonstrable value to an industry or government organization. This discovered knowledge must be validated as interesting and useful; it cannot simply be a model that has better performance on a standard metric such as accuracy or area under a curve. A new business insight or business model enabled by the use of data science techniques (e.g., the interests of users are similar to those of their friends in social networks) is an example of what this category will include.
* Foundational: Submissions do not have to be implemented, but must have clear applicability to distinguish them from KDD research papers. Submissions may also provide insight into issues and factors that affect the successful use and deployment of data science methods. Papers that describe enabling infrastructure for large-scale deployment of applied machine learning also fall in this category.

Submission directions
KDD is a dual track conference hosting both a Research track and an Applied Data Science track. Due to the large number of submissions, papers submitted to the Research track will not be considered for publication in the Applied Data Science track and vice versa. Authors are encouraged to read the track descriptions carefully and to choose an appropriate track for their submissions.

Following KDD conference tradition, reviews are not double-blind, and author names and affiliations should be listed. There will be an author response phase between submission and final decision.

Submissions are limited to 8 (eight) pages of content and 1 (one) page for references and must be in PDF format and formatted according to the standard double column ACM Proceedings Template, Tighter Alternate style. Additional information about formatting and style files are available online at: Papers that do not meet the formatting requirements will be rejected without review. Submitted papers will be assessed based on their novelty, technical quality, potential impact, clarity, and reproducibility.

Submissions site will open in Jan 2017. Please check back for further information.

Important policies
* Reproducibility
Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available whenever possible. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission.

* Authorship
Every person named as the author of a paper must have contributed substantially both to the work described in the paper and to the writing of the paper. Every listed author must take responsibility for the entire content of a paper. Persons who do not meet these requirements may be acknowledged, but should not be listed as authors. Changes to the author list after a submission has been accepted is not allowed.

* Dual submissions
Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or journals. However, there are several exceptions to this rule.
1) Submission to KDD is permitted of a shorter version of a paper that has been submitted to a journal, but has not yet been published in that journal. Authors must declare such dual-submissions on the CMT submission form. Authors must make sure that the journal in question allows dual concurrent submissions to conferences.
2) Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
3) Submission is permitted for papers that have previously been made available as a technical report or similar, in particular in arXiv.

* Conflicts of interest
During the submission process, enter the email domains of all institutions with which you have an institutional conflict of interest. You have an institutional conflict of interest if you are currently employed or have been employed at this institution in the past three years, or you have extensively collaborated with this institution within the past three years. Authors are also required to identify all PC/SPC members with whom they have a conflict of interest, eg, advisor, student, colleague, or coauthor in the last five years.

* Attendance
For each accepted paper, at least one author must attend the conference and present the paper. Authors of all accepted papers must prepare a final version for publication, a poster, and a three-minute short video presentation.

Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library. The rights retained by authors who transfer copyright to ACM can be found here.

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
IJCAI 2022   31st International Joint Conference on Artificial Intelligence
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
AI Meets Cybersecurity 2022   Call for Papers: AI Meets Cybersecurity from Journal of Intelligent Information Systems
MLDM 2022   18th International Conference on Machine Learning and Data Mining
KDD 2021   27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
CONF-CDS 2022   The 4th International Conference on Computing and Data Science (CONF-CDS) Call for Papers
ICML 2022   39th International Conference on Machine Learning
CiViE 2022   6th International Conference On Civil Engineering
Call for Papers 2022   Call for Papers Historical Yearbook 2022