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KDD 2010 : The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining


Conference Series : Knowledge Discovery and Data Mining
When Jul 25, 2010 - Jul 28, 2010
Where Washington DC, USA
Abstract Registration Due Feb 2, 2010
Submission Deadline Feb 5, 2010
Notification Due Apr 30, 2010

Call For Papers

The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2010) will be held on July 25-28, 2010 in Crystal City, Washington, DC. The conference will include two refereed paper tracks: the Research track, and the Industry & Government track; this document includes the call for papers for these tracks below.
Research Track - Call For Papers

We invite high-quality papers reporting original research on all aspects of knowledge discovery and data mining. We especially encourage submissions that promote the advancement of KDD as a scientific and engineering discipline and submissions that bridge between different disciplines. Papers are rigorously evaluated based on potential impact, novelty, repeatability and presentation.

Areas of interest include, but are not limited to:

* data mining algorithms (supervised, semi-supervised and unsupervised)
* data mining foundations and theory
* dimensionality reduction and feature selection
* mining dynamic and evolving data
* mining graph data
* mining semi-structured data
* mining spatial and temporal data
* mining stream data
* mixed-initiative data mining and active learning
* outlier analysis and anomaly detection
* parallel and distributed data mining algorithms
* pattern mining and association analysis
* robust and highly scalable data mining algorithms
* similarity search in data mining
* statistical methods in data mining
* topic models and matrix methods in data mining
* transfer learning and mining with auxiliary data sources
* adversarial data mining algorithms
* biological and medical data mining
* data mining for computational advertising
* data mining in social sciences and on social networks
* mining environmental and scientific data
* mining sensor data
* mining user behavioral and feedback data
* mining the Web and text data
* multimedia data mining
* data mining for other novel applications
* data integration and indexing for data mining
* data visualization for data mining
* KDD methodology and process
* platforms and systems for KDD
* pre-processing and post-processing in data mining
* security and privacy issues in data mining
* user modeling in data mining

All submitted papers will be judged based on their technical merit, rigor, significance, originality, repeatability, relevance, and clarity. Papers submitted to KDD'10 should be original work, not previously published in a peer-reviewed conference or journal. Substantially similar versions of the paper submitted to KDD'10 should not be under review in another peer-reviewed conference or journal during the KDD’10 reviewing period.

Repeatability guideline: Repeatability is a cornerstone of any scientific and engineering endeavor. To promote a solid foundation upon which future KDD work can be built, authors should make every effort to make code available as open source, and to employ public datasets, or make novel datasets available to the community. If this is not possible, please include a justification to that effect. Comparison to credible baseline systems and statistical significance of experimental results are expected for all papers with empirical evaluations.

Dates: abstract due on Feb 2, 2010; paper due on Feb 5, 2010. acceptance notification: April 30, 2010.

Submission Site: TBD.

For further information please contact the Program Chairs.
Industrial/Government Applications Track - Call For Papers

The Industrial/Government Applications Track of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’10) will highlight successful uses of KDD technology, including deployed applications incorporating KDD technologies and discoveries of valid, novel, understandable, and demonstrably useful patterns from large datasets in industry and government. It will also include papers that address challenges, lessons, concerns, and research issues arising out of attempts – both successful and unsuccessful – to deploy KDD technology for the solution of actual industry and government problems.

The KDD’10 Industrial/Government Applications (I/G) Track seeks to:


provide a forum for exchanging ideas between KDD practitioners, researchers, companies, and government organizations;

help industrial and government organizations highlight successful KDD applications;

raise interesting (research) challenges and other concerns more specific to industry and government -- customer privacy issues, analysis of data not generally available in academia, issues of scale that arise more heavily in a corporate setting, etc.

The I/G 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 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 in three sub-areas. Submitters should identify in which of these sub-areas their paper should be evaluated.

* Deployed KDD case studies
* Discoveries of knowledge with demonstrable value to industry or government
* Emerging applications and technology, including challenges and issues arising from attempts to deploy KDD technology to solve specific industry or government problems

Deployed KDD case studies describe deployed projects with measurable benefits that include KDD technology. These papers must clearly describe the industry or Government problem that is solved, the overall architecture of the deployed system, the data sources used, the reasons for the choices of particular KDD technologies, how KDD technologies solved the problem, the particular KDD process embodied by the deployed application, the use and payoff of the application, the costs to develop the application, the maintenance plan, and the number and types of users.

Papers that describe discoveries of knowledge must clearly state what data sources and background knowledge were used, what data mining algorithms were tried, what overall KDD process was used, what the new discovered knowledge is, how the new knowledge was validated, and what the value to the industry or government is of such newly discovered knowledge.

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 with other technologies. Such papers must clearly explain the requirements arising from the particular industry or government setting for which the application is being developed and from the particular databases on which the application is based. These papers must also identify how the emerging solution is using KDD technologies to address these requirements, the deployment plan, and the evaluation methodology and metrics for the emerging application. Pragmatic issues and considerations include important practical and research considerations, approaches, and architectures that enable successful applications. This category may include comparative evaluations of different KDD technologies for particular application problems. Preferences will be given to papers whose insights may generalize to other domains or problems. Product advertisements will not be accepted.

A new feature of this year’s I/G track is the inclusion of a video forum, in which the accepted authors can optionally include a video demonstration of their system. These videos will be posted online for effective dissemination of the result. Authors of the I/G track can optionally submit their videos at the time of paper submission as well. Accepted papers can revise and improve their submitted videos later.

For further information please contact the Industrial Track Chairs.

Related Resources

ADAH 2017   Advanced Data Analytics in Health
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
EiDMKD 2018   2018 International Conference on Data Mining and Knowledge Discovery(DMKD 2018)
MLDM 2018   14th International Conference on Machine Learning and Data Mining MLDM 2018
ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
KDD Cup 2017   Call for Proposals
ACM--ICKMS--Ei and Scopus 2018   ACM--2018 International Conference on Knowledge Management Systems (ICKMS 2018)--Ei Compendex and Scopus
ICCDE--Ei and Scopus 2018   ACM - 2018 International Conference on Computing and Data Engineering (ICCDE 2018)--Ei Compendex and Scopus