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KDD-2011 call for papers

Abstracts due Feb 11 for KDD-2011, 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug 21-24, 2011, in San Diego, CA http://www.kdd.org/kdd2011/

  • Feb 11, 2011 Paper Abstract Deadline
  • Feb 18, 2011 Full Paper Deadline
Follow us on Twitter (twitter.com/KDD2011), Facebook (www.facebook.com/event.php?eid=162069847153393) and LinkedIn (coming soon)

New for KDD 2011:
Industry Practice Expo: www.kdd.org/kdd2011/indexpo.shtml

Key Dates:

  • Aug 21-24, 2011 KDD-2011 Conference
  • June 30, 2011 KDD Cup Competition Ends
  • May 13, 2011 Paper Acceptance
  • Mar 15, 2011 KDD Cup Competition Begins
  • Mar 1, 2011 KDD Cup Registration Opens (www.kdd.org/kdd2011/kddcup.shtml)
  • Mar 1, 2011 Notification of Workshop Decisions
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.

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 sub-areas their paper should be evaluated as distinct review criteria will be used to evaluate each category of submission.

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)

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