DDDM 2011 - Workshop on Domain Driven Data Mining

Website icdm2011.cs.ualberta.ca | Edit Freely

Category DDDM 2011

Deadline: July 23, 2011 | Date: December 10, 2011

Venue/Country: Vancouver, Canada

Updated: 2011-05-29 20:04:37 (GMT+9)

Call For Papers - CFP

Following the success of DDDM2009 and DDDM2010, DDDM2011 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/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.