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Notice

AISB event Bulletin Item

CFP: KDD'07 Workshop on Domain Driven Data Mining

http://datamining.it.uts.edu.au/dddm/

Call for Papers
 2007 ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM 2007)
           San Jose, California, USA, 12th August 2007

    In conjunction with ACM SIGKDD International Conference on
           Knowledge Discovery and Data Mining (KDD'07)

            URL: http://datamining.it.uts.edu.au/dddm/

********************************************************************


The "2007 ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM2007)"
aims to provide a premier forum for sharing findings, knowledge,
insight, experience and lessons in tackling potential challenges in
discovering actionable knowledge from complex domain problems,
promoting interaction and filling the gap between academia and
business, and driving a paradigm shift from interesting hidden
pattern mining to actionable knowledge discovery in varying data
mining domains.

The workshop welcomes theoretical and applied disseminations that
make efforts (1) to expose next-generation data mining methodology
for actionable knowledge discovery, identifying how KDD techniques
can better contribute to critical domain problems in theory and
practice; (2) to uncover domain-driven data mining techniques
identifying how KDD can better strengthen business intelligence in
complex enterprise applications; (3) to disclose the applications of
domain-driven data mining identifying how KDD can be effectively
deployed into solving complex practical problems; and (4) to identify
challenges and directions for future research and development in the
dialogue between academia  and business.


Topics
------

The SIGKDD DDDM2007 Workshop solicits original theoretical and
practical research on topics including, but not limited to, the
following aspects:
* Domain-driven data mining methodology and project management
* Domain-driven KDD infrastructure and system support
* Knowledge actionability and actionable knowledge discovery framework
* Involvement of human intelligence, domain intelligence,
  web intelligence
* Mining in-depth patterns and deep data intelligence
* Intelligence meta-synthesis in KDD
* Human-centered mining and human-mining interaction
* Activity, impact, event, process and workflow mining
* Unbalanced, constraint, dynamic and stream mining
* Reliability, trust, privacy, utility, issues in data mining
* Enterprise-oriented, spatio-temporal, multiple source mining
* Ontology and knowledge engineering and management
* Computational performance and actionability enhancement
* Domain specific mining such as security mining, bioinformatic
  mining, etc.


Submission Instructions
-----------------------
Interested authors should submit their papers as an email attachment
to the Co-Chair (kdd at it.uts.edu.au) of the ACM SIGKDD Workshop on
Domain Driven Data Mining (DDDM 2007). Submissions should be prepared
using the standard ACM format
(http://www.acm.org/sigs/pubs/proceed/template.html) in MS Word or PDF
format. Manuscripts must not exceed 10 pages, and present a cover page
which should include the paper title, author(s), authors' affiliations,
e-mail addresses, contact numbers, postal address, and an abstract.


Important dates
---------------

Submission of Papers: Extended to 31 May
Notification: June 20, 2007
Camera Ready: July 10, 2007
Half-day Workshop Presentation: August 12, 2007


Publication Plans
----------------------

The workshop proceedings will be published by Springer.


Organizing Committee
--------------------

General Chair
   Philips Yu, IBM T.J. Watson Research Center, USA

Workshop Chairs
   Chengqi Zhang, University of Technology, Sydney, Australia
   Graham Williams, Australian Taxation Office, Australia
   Longbing Cao, University of Technology, Sydney, Australia

Organizing Chair
   Yanchang Zhao, University of Technology, Sydney, Australia

Webmaster
   Chao Luo, University of Technology, Sydney, Australia


Contacts
--------

For general questions: Longbing Cao (lbcao at it.uts.edu.au)
For paper submission: Yanchang Zhao (yczhao at it.uts.edu.au)
For website: Chao Luo (chaoluo at it.uts.edu.au)