KDD-2012 CALL FOR PAPERS
18th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining
August 12-16, 2012, Beijing, China
- Papers due: February 10, 2012
- Acceptance notification: May 4, 2012
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.
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 conference CFP and choose an appropriate track for their submissions. In case of doubts, authors are encouraged to get in touch with the chairs of the corresponding track at least a week before the submission deadline.
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): association analysis, classification and regression methods, semi-supervised learning, clustering, factorization, transfer and multi-task learning, feature selection, social networks, mining of graph data, temporal and spatial data analysis, scalability, privacy, security, visualization, text analysis, Web mining, mining mobile data, recommender systems, bioinformatics, e-commerce, online advertising, anomaly detection, and knowledge discovery from big data, including the data on the cloud. Papers emphasizing theoretical foundations, novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications are particularly encouraged. 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. 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.
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, environment, 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.
Research Program Co-chairs:
- Deepak Agarwal, Yahoo! Research
- Jian Pei, Simon Fraser University
- Michael Zeller (Zementis)
- Hui Xiong (Rutgers University)
- Qiang Yang, HKUST
- Dou Shen, CityGrid Media