DDDM 2011 - Workshop on Domain Driven Data Mining

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 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.