We solicit 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 applied work addressing real-world problems and systems demonstrating tangible impact/value in their respective domains (eg. industries, government initiatives, social programs).
Submissions must clearly identify the category they fall into:: “deployed” or “evidential. 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 (or was) in production use for an extended period of time. 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 from successes and failures . 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.
Examples from past KDD conferences:
CATEGORY Evidential : Must describe fundamental insights derived from addressing a significant real-world problem, even though a system has not been deployed. This might include papers providing significant gains in the understanding of a applied area/domain (for example, involving data or system deployment needs) or even papers where a conclusion has been reached that the problem is unsolvable. In addition to insights the paper must explain what milestones were reached, what the practical impact is, and (if applicable) what the obstacles to deployment are. Straightforward improvements over trivial baseline solutions are unlikely to qualify. Continuing the example above, a paper in this category 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.
- HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network
- Cascade Ranking for Operational E-commerce Search
Examples from past KDD conferences:
Please consult the guidelines for authors here.
- DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
- A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments Backpage and Bitcoin: Uncovering Human Traffickers
- TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
- Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem
Submission topics include but are not limited to:
Target application area—Business
Target application area—Life Sciences
Target application area—Social and Network Sciences
Target application area—Facilitating the Learning Process
Further target application areas
- Advertising and E-commerce
- Markets and Crowds
- Recommender systems
- Clinical Decision Support
- Clinical Research
- Healthcare and Caregiving
- Patient Empowerment
- Network sciences
- Social good
- Social media and publishing
- Social sciences
- User modeling
- Web mining
- Big Data infrastructures
- Cloud, Map-Reduce, MPI
- Data protection
- Design of experiments
- Interpretable models
- Large-scale optimization
- Scalable algorithms
- Mobile and Sensor devices