[dbjapan] KDD-2009: CALL FOR PAPERS

[Satoshi Oyama <oyama [at] i.kyoto-u.ac.jp>]

[Apologies if you receive this more than once]

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KDD-2009: The Fifteenth ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD'09)

Paris, France
June 28 - July 1, 2009.

http://www.kdd.org/kdd2009/

CALL FOR PAPERS

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The annual ACM SIGKDD conference is the premier international forum for data
mining researchers and practitioners from academia, industry, and government
to share their ideas, research results and experiences. KDD-09 will feature
keynote presentations, oral paper presentations, poster sessions, workshops,
tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.

We invite submissions on all aspects of knowledge discovery and data mining.
We especially encourage papers relevant to KDD that cut across disciplines
such as machine learning, pattern recognition, statistics, databases, theory,
mathematical optimization, data compression, cryptography, and high performance
computing. 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.


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Important dates:
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***Note the earlier submission deadlines***
Abstract submission: February 2, 2009
Paper submission: February 6, 2009
Notification: April 10, 2009
Conference dates: June 28 - July 1, 2009

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Areas of interest include, but are not limited to:
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Novel data mining algorithms
Data mining foundations
Innovative applications of data mining
Data mining and KDD systems and frameworks
Mining data streams and sensor data
Mining multi-media data
Mining social networks and graph data
Mining spatial and temporal data
Mining biological and biomedical data
Mining text, Web, semantic web and semi-structured data
Mining dynamic data
Pre-processing and post-processing in data mining
Robust and scalable statistical methods
Security, privacy, and adversarial data mining
High performance and parallel/distributed data mining
Mining tera-/peta-scale data
Visual data mining and data visualization
Data integration issues in mining
Data and knowledge provenance in KDD


All submitted papers will be judged based on their technical
merit, rigor, significance, originality, repeatability,
relevance, and clarity. Papers submitted to KDD'09 should be
original work, not previously published in a peer-reviewed
conference or journal. Substantially similar versions of the
paper submitted to KDD'09 should not be under review in another
peer-reviewed conference or journal during the KDD-09
reviewing period.

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Repeatability guideline:
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Repeatability is a cornerstone of any scientific endeavor. To
ensure the long term viability of the research output of the SIGKDD
community, we require open-source/public distribution of the code
and the datasets. In those cases where this is not possible due to
proprietary considerations, every effort should be made to provide
the binary executable. If proprietary datasets are used, every effort
should also be made to apply the approach to similar publicly
available datasets. Furthermore, the description of experimental
results in submitted papers should be accompanied by all relevant
implementation details and exact parameter specifications.


Peter Flach and Mohammed J. Zaki
KDD'09 Program Co-Chairs

John Elder and Francoise Soulie Fogelman
General Chairs