17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011)
August 21-24, 2011
Manchester Grand Hyatt
San Diego, CA

Key Dates:
Abstracts due: February 11, 2011
Papers due: February 18, 2011
Acceptance notification: May 1, 2011

Paper submission and reviewing will be handled electronically. Authors should consult the conference Web site for full details regarding paper preparation and submission guidelines.

Papers submitted to KDD 2011 should be original work and substantively different from papers that have been previously published or are under review in a journal or another peer-reviewed conference.

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining. Examples of topic of interest include (but are not limited to): classification and regression methods, semi-supervised learning, clustering, feature selection, social networks, mining of graph data, temporal and spatial data analysis, scalability, privacy, visualization, text analysis, Web mining, recommender systems, and so on. 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. We welcome submissions by authors who are new to the KDD conference, as well as visionary papers on new and emerging topics. Authors are explicitly discouraged from submitting papers that contain only incremental results and that do not provide significant advances over existing approaches.

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 when possible.

As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

The Industrial/Government Applications Track solicits papers describing implementations of KDD solutions relevant to industrial or government settings. The primary emphasis is on papers that advance the understanding of practical, applied, or pragmatic issues related to the use of KDD technologies in industry and government and highlight new research challenges arising from attempts to create such real KDD applications. Applications can be in any field including, but not limited to: e-commerce, medical and pharmaceutical, defense, public policy, engineering, manufacturing, telecommunications, and government.

The Industrial/Government Applications Track will consist of competitively-selected contributed papers. Submitters must clearly identify in which of the following three areas their paper should be evaluated as distinct review criteria will be used to evaluate each category of submission. The Author Instructions (Industry Track) explicitly list the required elements for a paper in each of these areas, referred to in shorthand as "deployed", "discovery", and "emerging". Authors are also strongly encouraged to review the Foreword to the KDD2010 Industrial/Government track for additional insight into the emphases and intent of the track and descriptions of the type of papers that are likely to be of interest.

  • Deployed KDD systems that are providing real value to industry, Government, or other organizations or professions. These deployed systems could support ongoing knowledge discovery or could be applications that employ discovered knowledge, or some combination of the two.
  • Discoveries of knowledge with demonstrable value to Industry, Government, or other users (e.g., scientific or medical professions). This knowledge must be "externally validated" as interesting and useful; it can not simply be a model that has better performance on some traditional KDD metric such as accuracy or area under the curve.
  • Emerging applications and technology that provide insight relevant to the above value propositions. These emerging applications must have clear user interest and support to distinguish them from KDD research papers, or they must provide insight into issues and factors that affect the successful use of KDD technology and methods. Papers that describe infrastructure that enables the large-scale deployment of KDD techniques also are in this area.
  • General Chair:
    • Chid Apte (IBM Research)
  • Research Program Co-chairs:
    • Joydeep Ghosh (University of Texas, Austin)
    • Padhraic Smyth (University of California, Irvine)
  • Industry and Government Program Co-chairs:
    • Ted Senator (SAIC)
    • Michael Zeller (Zementis)