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KDD-2013 Call for Papers


KDD-2013, 19th ACM SIGKDD Knowledge Discovery and Data Mining Conference, is the premier conference on data mining and data science. KDD is a dual track conference hosting both a research track and an industrial/government track. Abstracts due Feb 15.



KDD-201319th ACM SIGKDD Knowledge Discovery
and Data Mining Conference

Call For Papers
August 11-14, 2013, Chicago, USA

www.kdd.org/kdd2013

KEY KDD 2013 DATES

  • Abstracts due: Feb 15, 2013
  • Papers due: Feb 22, 2013
  • Acceptance notification: May 10, 2013
  • Conference: Aug 11-14, 2013

About KDD
ACM KDD is the premier conference on data mining.

Paper submission and reviewing to KDD-2013 will be handled electronically. The paper length is limited to 9 pages, including references, diagrams, and appendices, if any. The format is the standard double-column ACM Tighter Alternate Proceedings Style. As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed. Authors should consult the conference Website for full details regarding paper preparation and submission guidelines.

Papers submitted to KDD-2013 should be original work and substantively different from papers that have been previously published or are under review in a journal or another conference/workshop. Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library.

KDD is a dual track conference hosting both a research track and an industrial/government track. Due to the large number of submissions, papers submitted to the research track will not be considered for publication in the industry/government track and vice-versa. Authors are encouraged to carefully read the following and choose an appropriate track for their submissions.

CALL FOR PAPERS - Research Track

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining.

Examples of topics of interest include, but are not limited to: social network analysis, classification and regression methods, semi-supervised learning, unsupervised learning and clustering, graph and link mining, rule and pattern mining, web mining, efficient or distributed mining algorithms, mining temporal and spatial data, probabilistic methods, text mining, security and privacy, optimization techniques, recommender systems, mining sequences, topic and graphical models, matrix and tensor methods, feature selection, online advertising, biological and medical data, anomaly detection, and knowledge discovery from big data.

Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications. Visionary papers on new and emerging topics are also welcome. Authors are explicitly discouraged from submitting papers that contain only incremental results and that do not provide significant advances over existing approaches.

Application oriented papers that make innovative technical contributions to research are welcome.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. 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.

CALL FOR PAPERS - Industrial and Government Track

We invite submissions describing implementations of data mining and analytics systems in industrial or government settings. The primary emphasis is on papers that advance the understanding and dealing of practical issues related to deploying analytics technologies in industry and government and highlight new research challenges motivated by analytics and data mining applications. These applications can be in any field including, but not limited to e-commerce, medicine, healthcare, defense, public policy, engineering, law, manufacturing, telecommunications, and government.

Submitted papers will go through a competitive peer review process. We would like to differentiate the Industry & Government track from the Research Track by ensuring that submissions solve real-world problems and focus on deployments.

Submissions must clearly identify in which one of the following three areas they should be evaluated: "deployed", "discovery", and "emerging".

The criteria for submissions in each category is as follows:

Deployed category must describe deployment of a system that solves a non-trivial real-world problem. The focus should be on describing the problem, it's significance, decisions and tradeoffs made when making design choices for the solution, deployment challenges, and lessons learned.

Discovery category 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.

Emerging category 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.

On Behalf of the KDD-2013 Organizers:

Research Program Co-Chairs:

  • Inderjit Dhillon (The University of Texas at Austin)
  • Yehuda Koren (Google)

Industry and Government Program Co-Chairs:

  • Rayid Ghani (Obama for America)
  • Ted Senator (SAIC)

General Chairs:

  • Robert Grossman (University of Chicago and Open Data Group)
  • Ramasamy Uthurusamy

www.kdd.org/kdd2013


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