Domain Driven Data Mining

Domain Driven Data Mining investigates issues surrounding actionable knowledge discovery and delivery, including but is not limited to the following topics: 

 (1) Methodologies and infrastructures 

• 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) Delivery 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.