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DDDM 2011 : CFP: DDDM 2011 Workshop joint with ICDM 2011, Deadline July 23, Vancouver, Canada


When Dec 11, 2011 - Dec 14, 2011
Where Vancouver, Canada
Submission Deadline Jul 23, 2011
Notification Due Sep 20, 2011
Final Version Due Oct 11, 2011
Categories    data mining   machine learning   artifical intelligence   databases

Call For Papers


Call for Papers ***Apologies for cross-posting***

The 5th International Workshop on Domain Driven Data Mining (DDDM)
In conjunction with the 2011 IEEE International Conference on Data Mining (ICDM 2011)
December 11-14, 2011, Vancouver, Canada

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.

All papers accepted by the workshop will be included in the ICDM'10 Workshop Proceedings published by the IEEE Computer Society Press.

* Important Dates:
• Submission Deadline: July 23, 2011
• Notification of Acceptance: September 20, 2011
• Camera Ready Submission Due: October 11, 2011

* Topics:
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/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.

* Keynote Speaker:
• Jian Pei, Simon Fraser University

* Organizing Committee:
• General chair:
- Phillips Yu, University of Illinois at Chicago

• PC co-chairs:
- Wei Fan, IBM T.J. Watson Research
- Wolfgang Nejdl, L3S Research Center, University of Hannover
- Ling Chen, University of Technology Sydney

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