KDD 2012 : KDD-2012: Call for Industry and Government Track Papers
Call For Papers
CALL FOR PAPERS (INDUSTRY & GOVERNMENT TRACK)
18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2012)
August 12-16, 2012
Papers due: February 10, 2012
Acceptance notification: May 4, 2012
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 2012 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.
As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.
INDUSTRY & GOVERNMENT TRACK
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, finance, 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.
Michael Zeller (Zementis)
Hui Xiong (Rutgers University)
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