KDD 2019 Call for Research, Applied Data Science Papers
KDD2019 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 3.
By KDD 2019 Organizers.
 KDD 2019 Call for Applied Data Science Papers, Deadline: February 3, 2019
 KDD 2019 Call for Research Papers, Deadline: February 3, 2019
 KDD Cup 2019 Call for Proposals, Deadline: February 10, 2019
The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, largescale data analytics, and big data.
KDD2019
August 48, 2019
Anchorage, Alaska, USA
Dena’ina Convention Center and William Egan Convention Center
We invite 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 applicationoriented 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: Largescale systems for text and graph analysis, machine learning, optimization, parallel and distributed data mining (cloud, mapreduce), 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, spatiotemporal 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, semisupervised 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. Submissions are limited to a total of nine (9) pages, including all content and references, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. For LaTeX users: unzip acmart.zip, make, and use samplesigconf.tex as a template;
Additional information about formatting and style files is available online at: https://www.acm.org/publications/proceedingstemplate.
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