Continuing the tradition of bridging the gap between research and application, we are excited to call for participation in two flavors of tutorials at KDD 2017:

1. Traditional Tutorials
2. Hands-on Tutorials.

A Traditional Tutorial will cover the state-of-the-art research, development and applications in a specific data mining related area, and stimulate and facilitate future work. Tutorials on interdisciplinary directions, novel and fast growing directions, and significant applications are highly encouraged. We also encourage tutorials in areas that are somewhat different from the usual KDD mainstream, but still very much related to KDD mission and objectives of gaining insight from data.

A Hands-on Tutorial will feature in-depth hands-on training on cutting edge systems and tools of relevance to data mining and machine learning community. These sessions are targeted at novice as well as moderately skilled users. The focus should be on providing hands-on experience to the attendees. The pace of the tutorial should be set such that beginners can follow along comfortably. The tools & systems themselves must be available under open-source licenses (e.g. Apache 2.0, MIT, BSD etc.) and have proven track record of success in the community. Tutorials should introduce the motivation behind the tool, associated fundamental concepts, and work through examples and demonstrate its application to relatable real-life use cases.

The KDD 2017 organizing committee solicits proposals for full-day and half-day workshops to be held in conjunction with the main conference. The purpose of a workshop is to provide an opportunity for participants from academia, industry, government and other related parties to present and discuss novel ideas on current and emerging topics relevant to knowledge discovery and data mining. Workshops are scheduled for August 14, 2017.

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.

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 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.