22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is a premier conference that brings together researchers and practitioners from data mining, knowledge discovery, data science, and big data. KDD 2016 will be held in dynamic San Francisco, California during August 13-17, 2016. Submissions for Research track and Practice track are solicited for KDD 2016 on all aspects of knowledge discovery and data mining.
- Paper submission due: February 12, 2016
- Decision notification: May 12, 2016
Tracks: KDD is a dual track conference hosting both a Research track and a 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 carefully read the track descriptions and choose an appropriate track for their submissions.
Evaluation and decision criteria: As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible. Papers will be reviewed by members of the KDD program committee and decisions will be emailed to all authors by May 12, 2016. Note that there will not be an author response phase between submission and decisions.
Formatting requirements: Papers are limited to 10 pages, including references, diagrams, and appendices, if any. The format is the standard double column ACM Proceedings Template, Tighter Alternate style. Additional information about formatting and style files are available online at: http://www.acm.org/sigs/publications/proceedings-templates. Note: Papers that do not meet the formatting requirements will be rejected without review. Dual submission policy: Submitted papers must describe work that is substantively different from work that has already been published or is currently under review for another conference/journal. In particular, papers submitted to KDD should be substantively different to any papers submitted to another conference/journal where the review and decision period of the other conference/journal overlaps with that of KDD.
Research track papers
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 significant advances over existing approaches.
Papers submitted to the Research Track are solicited in all areas of data mining, knowledge discovery, and large-scale data analytics, including, but not limited to:
- Big Data: Efficient and distributed data mining platforms and algorithms, systems for large-scale data analytics of textual and graph data, large-scale machine learning systems, distributed computing (cloud, map-reduce, MPI), large-scale optimization, and novel statistical techniques for big data.
- Data Science: Methods for analyzing scientific data, business data, social network analysis, recommender systems, mining sequences, time series analysis, online advertising, bioinformatics, systems biology, text/web analysis, mining temporal and spatial data, and multimedia processing.
- Foundations of Data Mining: Data mining methodology, data mining model selection, visualization, asymptotic analysis, information theory, security and privacy, graph and link mining, rule and pattern mining, web mining, dimensionality reduction and manifold learning, combinatorial optimization, relational and structured learning, matrix and tensor methods, classification and regression methods, semi-supervised learning, and unsupervised learning and clustering.
Applied Data Science track papers
We invite submissions of papers describing research and implementations of data mining/data analytics/big data/data science solutions and systems for practical tasks and practical settings. The application domains of interest include, but are not limited to education, public policy, industry, government, healthcare, e-commerce, telecommunications, law, or non-profit settings. Our primary emphasis is on papers that advance the understanding of, and show how to deal with, practical issues related to deploying analytics technologies. This track also highlights new research challenges motivated by analytics and data mining applications in the real world.
Submitted papers will go through a competitive peer review process. The Practice Track (formerly known as the “Industry and Government 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 “emerging”.
The criteria for submissions in each category are as follows:
- Deployed: Must describe deployment of a system that solves a non-trivial 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 discoveries with demonstrable value to an industry or government organization. This discovered knowledge must be “externally validated” as interesting and useful; it can not simply be a model that has better performance on some traditional evaluation metrics such as accuracy or area under the curve. A new scientific discovery enabled by the use of data mining techniques is an example of what this category will include.
- Emerging: Submissions do not have to be deployed but must have clear applications to Industry/ Government to distinguish them from KDD research papers. They may also provide insight into issues and factors that affect the successful use and deployment of Data Mining and Analytics. Papers that describe enabling infrastructure for large-scale deployment of Data Mining and analytics techniques also fall in this category.