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Call for Papers - DDDM 2009
The 3rd International Workshop on Domain Driven Data Mining
Miami, Florida, USA, December 6, 2009
In conjunction with IEEE ICDM'09
URL: http://datamining.it.uts.edu.au/dddm09/
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The Workshop on Domain Driven Data Mining (DDDM) series
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 data-centered hidden pattern mining
to domain-driven actionable knowledge delivery in varying
data mining domains toward supporting smart decision and
businesses.

Following the success of DDDM2007 joint with SIGKDD2007 in
the US and DDDM2008 joint with ICDM2008 in Italy, DDDM2009
welcomes theoretical and applied disseminations that make
efforts:

- to design next-generation data mining methodology for
actionable knowledge discovery and delivery, toward
handling critical issues for KDD to effectively and
efficiently contribute to real-world smart businesses and
smart decision and benefit critical domain problems in
theory and practice;

- to devise domain-driven data mining techniques to bridge
the gap between a converted problem and its actual
business problem, between academic objectives and
business goals, between technical significance and
business interest, and between identified patterns and
business expected deliverables, toward strengthening
business intelligence in complex enterprise applications;

- to present the applications of domain-driven data mining
and demonstrate how KDD can be effectively deployed to
solve complex practical problems; and

- to identify challenges and future directions for data
mining research and development in the dialogue between
academia and industry.



TOPICS OF INTEREST
==================

This workshop solicits original theoretical and practical
research on the following topics.

(1) Methodologies and infrastructure
- Domain-driven data mining methodology and project
management
- Domain-driven data mining framework, system support and
infrastructure


(2) Ubiquitous intelligence
- Involvement and integration of human intelligence, domain
intelligence, network intelligence, organizational
intelligence and social intelligence in data mining
- Explicit, implicit, syntactic and semantic intelligence
in data
- Qualitative and quantitative domain intelligence
- In-depth patterns and knowledge
- Human social intelligence and animat/agent-based social
intelligence in data mining
- Explicit/direct or implicit/indirect involvement of human
intelligence
- Belief, intention, expectation, sentiment, opinion,
inspiration, brainstorm, retrospection, reasoning inputs
in data mining
- Modeling human intelligence, user preference, dynamic
supervision and human-mining interaction
- Involving expert group, embodied cognition, collective
intelligence and consensus construction in data mining
- Human-centered mining and human-mining interaction
- Formalization of domain knowledge, background and prior
information, meta knowledge, empirical knowledge in data
mining
- Constraint, organizational, social and environmental
factors in data mining
- Involving networked constituent information in data
mining
- Utilizing networking facilities for data mining
- Ontology and knowledge engineering and management
- Intelligence meta-synthesis in data mining
- Domain driven data mining algorithms
- Social data mining software


(3) Deliverable and evaluation
- Presentation and delivery of data mining deliverables
- Domain driven data mining evaluation system
- Trust, reputation, cost, benefit, risk, privacy, utility
and other issues in data mining
- Post-mining, transfer mining, from mined patterns and
knowledge to operable business rules.
- Knowledge actionability, and integrating technical and
business interestingness
- Reliability, dependability, workability, actionability
and usability of data mining
- Computational performance and actionability enhancement
- Handling inconsistencies between mined and existing
domain knowledge


(4) Enterprise applications
- Dynamic mining, evolutionary mining, real-time stream
mining, and domain adaptation
- Activity, impact, event, process and workflow mining
- Enterprise-oriented, spatio-temporal, multiple source
mining
- Domain specific data mining, etc.



Important Dates
===============

July 17, 2009 Due date for full workshop papers
Sept. 8, 2009 Notification of paper acceptance
Sept. 28, 2009 Camera-ready of accepted papers
Dec. 6, 2009 Workshop date



Submission
==========

Paper submissions should be limited to a maximum of 10
pages in the IEEE 2-column format, the same as the
camera-ready format (see the IEEE Computer Society Press
Proceedings Author Guidelines). All papers will be reviewed
by the Program Committee on the basis of technical quality,
relevance to domain driven data mining, originality,
significance and clarity.

All papers accepted for the workshop will be included in
the ICDM'09 Workshop Proceedings published by the IEEE
Computer Society Press. Selected papers from the workshop
will be invited for consideration of publication in a
special issue of a SCI-indexed journal to be confirmed.



Organizing Committee
====================

General Chair
Philip S Yu University of Illinois at Chicago, USA

Workshop Chairs
Longbing Cao University of Technology, Sydney, Australia
Jean-Francois Boulicaut University of Lyon, France
Shusaku Tsumoto Shimane University, Japan

Organizing Chair
Yanchang Zhao University of Technology, Sydney, Australia

Webmaster
Xuchun Su University of Technology, Sydney, Australia



Contact
=======
Inquiries can be forwarded to kdd(at)it.uts.edu.au.

For more information, please refer to the DDDM2009 website:
http://datamining.it.uts.edu.au/dddm09/