KDD 2018 Call for Research, Applied Data Science Papers

KDD-2018 invites submission of papers describing innovative research on all aspects of data science, and of applied papers describing designs and implementations for practical tasks in data science. Submissions due Feb 11.

KDD-2018 KDD-2018, to be held in London, 19-23 August, 2018, invites submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide major advances over existing approaches.

Topics of interest include, but are not limited to:
  • Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, parallel and distributed data mining (cloud, map-reduce), novel algorithmic and statistical techniques for big data.
  • Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
  • Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning; manifold learning, classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization.
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. Submissions are limited to a total of 9 (nine) pages, including all content and references, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template.

Submissions due: Feb 11, 2018

For details, see www.kdd.org/kdd2018/calls/view/kdd-2018-call-for-research-papers

research2018@kdd.org, Chih-Jen Lin and Hui Xiong

Research Track PC Chairs of KDD-2018

KDD 2018 Call for Applied Data Science Papers

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. The primary emphasis is on papers that either solve or advance the understanding of issues related to deploying data science technologies in the real world.

Submitted papers will go through a peer review process.

The Applied Data Science Track is distinct from the Research Track in that submissions focus on real-world problems and systems that are deployed or are in the process of being deployed. Submissions must clearly identify one of the following three categories they fall into: "deployed", "in-progress", or "observational". The ADS Chairs might shift a submission from one category to another, if they find that the submission is misplaced. The criteria for submissions in each category are as follows:

CATEGORY Deployed: Must describe implementation of a system that solves a significant real-world problem and is in current use. The paper should present the problem, its significance to the application domain, the decisions and tradeoffs made when making design choices for the solution, the deployment challenges, and the lessons learned. Evidence must be provided that the solution has been deployed by quantifying post-launch performance. Papers that describe enabling infrastructure for large-scale deployment of applied machine learning also fall in this category. An example might be a deployed system that collects heartbeat audio from mobile phones during a marathon race and uses machine learning to identify potentially irregular signals and to alert support personnel.

CATEGORY In-progress: As above, except that the system has not been deployed yet or (rarely!) a conclusion has been reached that the problem is unsolvable. In addition to the content required for deployed solution papers, in-progress papers must explain what fundamental insights were achieved so far, what milestones were reached, and what are the obstacles to deployment. Straightforward improvements over trivial baseline solutions are unlikely to qualify. Continuing the example above, an in-progress paper might present a system that achieves reasonable error rates in an experiment with many volunteers but suffers from interferences among mobiles that are located very close to each other.

CATEGORY Observational: Must describe important insights into the input data and/or the performance of a significant real world applications and explain their practical impact. Continuing the example above, an observational paper might present an analysis of a large number of recordings taken during in-hospital cardiac stress tests juxtaposing the system's performance to that of human cardiologists, the correlation between "murmurs" and other heart tests, the size of the models that can analyze the audio satisfactorily, etc, and from here derive whether such a system is currently realistic or will have to wait for better sensors and more on-device computing power.

Submissions due: Feb 11, 2018

For details, see