Important Dates
Paper Submission
We cannot accept submissions by e-mail, fax or postal mail. Research Track PapersCall for PapersPlease take note of the repeatability guidline.We invite submissions on all aspects of knowledge discovery and data mining overlapping with topics from machine learning, statistics, databases, and pattern recognition. Papers are expected to describe innovative ideas and solutions that are rigorously evaluated and well-presented. Submissions that describe minor variations of existing methods or only make small or questionable improvements to existing algorithms are discouraged. Areas of interest include, but are not limited to:
All submitted papers will be judged based on their technical merit, rigor, significance, originality, relevance, and clarity. Papers submitted to KDD-08 should be original work, not previously published in a peer-reviewed conference or journal. Papers substantially similar to papers submitted to KDD-08 should not be under review in another peer-reviewed conference or journal during the KDD-08 reviewing period. Repeatability guideline: As the SIGKDD conference enters its fourteenth year of existence, we need to take steps to ensure the long term viability of the research output of this community. A basic requirement is to enable the careful scrutiny and repeatability of evaluation results reported in a paper. The description of experimental results in submitted papers should be accompanied with all relevant implementation details and exact parameter specifications. Reviewers will be encouraged to downgrade ratings of papers that do not meet this guideline. Datasets used in the experiments should be made publicly available, whenever possible. When you must use proprietary datasets, please make every effort to supplement your results with those from closely matching synthetic datasets or other public datasets. Industrial/Government Applications TrackCall for PapersThe Industrial/Government Applications Track of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008) will highlight challenges, lessons, concerns, and research issues arising out of deploying applications of KDD technology. The focus is on promoting the exchange of ideas between researchers and practitioners of data mining. The KDD-2008 Industrial/Government Applications (I/G) Track seeks to:
The I/G Applications Track solicits papers describing implementations of KDD solutions relevant to commercial or government settings. The primary emphasis is on papers that advance our understanding of practical, applied, or pragmatic issues and highlight new research challenges in 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 I/G Applications Track will consist of competitively-selected contributed papers - presented in oral and/or poster form - as well as invited talks. We envision submissions along four sub-areas:
Emerging application and technology papers discuss prototype applications, tools for focused domains or tasks, useful techniques or methods, useful system architectures, scalability enablers, tool evaluations, or integration of KDD and other technologies. Case studies describe deployed projects with measurable benefits that include KDD technology. Such papers need to demonstrate the importance and impact of the work clearly. Comparative studies compare and contrast KDD technologies using specific examples (without being a product advertisement). Pragmatic issues and considerations include important practical and research considerations, approaches, and architectures that enable successful applications. Submitters are encouraged (but not required) to select one (or more) of these sub-areas for their papers. In their submission, authors are required to explain why the application is important, the specific need for KDD technology to solve the problem (including why other methods perhaps not based on data mining may fall short), and any innovations or lessons learned in the solution.
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