Domain Driven Data Mining

Towards Domain-Driven, Actionable Knowledge Discovery and Delivery

Topics

Domain Driven Data Mining investigates issues surrounding actionable knowledge discovery and delivering actionable intelligence, which include but are not limited to the following topics: 

 (1) Methodologies and infrastructures 

  • Domain-driven data mining/analytics/science methodology and project management 
  • Domain-driven data mining/analytics/science frameworks, system support and infrastructures

(2) Ubiquitous intelligences

  • Involvement and integration of data intelligence, human intelligence, domain intelligence, network intelligence, organizational intelligence, and social intelligence in data mining/analytics/science.
  • Explicit, implicit, syntactic and semantic data intelligence
  • Qualitative and quantitative domain knowledge and intelligence
  • Deep insights, knowledge and intelligence
  • Human social intelligence and animat/agent-based social intelligence
  • Explicit/direct and implicit/indirect involvement of human intelligence
  • Belief, intention, expectation, sentiment, opinion, inspiration, brainstorm, retrospection, reasoning inputs
  • Modeling human intelligence, user preference, dynamic supervision and human-mining interaction
  • Expert group and their knowledge, embodied cognition, collective intelligence and Consensus construction
  • Human-centered data mining/knowledge discovery/AI/data science and human-system interaction
  • Formalization of domain knowledge, background and prior information, meta knowledge, empirical knowledge
  • Constraint, organizational, social and environmental factors
  • Involving networked constituents and information
  • Utilizing networking facilities and resources
  • Ontology and knowledge engineering and management
  • Intelligence metasynthesis
  • Domain driven actionable knowledge discovery algorithms and models
  • Social data mining/analytics software

(3) Algorithms and enterprise applications

  • Statistical, mathematical methods for domain knowledge modeling, domain-specific data analysis, and domain adaptation, etc.
  • Deep representation of domain knowledge, expert knowledge and domain/social/organizational factors, etc.
  • Deep learning of domain-specific problems and applications, etc.
  • Dynamic, evolutionary, real-time, streaming, online and adaptive data mining/analytics
  • Activity, impact, event, process and workflow mining/analytics
  • Enterprise, large-scale, multisource, multimodal and multidomain data mining/analytics/science
  • Domain-specific data-driven applications and case studies, etc.

(4) Evaluation and deliverable

  • Presentation and delivery of data-driven discoveries, knowledge, insights, and intelligence
  • Subjective, objective, statistical and business-oriented evaluation systems
  • Ethics, trust, reputation, cost, benefit, risk, privacy, utility and other issues
  • Post-mining and knowledge transfer from discovered patterns/knowledge to operable business rules
  • Knowledge actionability, and integrating technical and business evaluation measures
  • Reliability, dependability, workability, interpretability and usability of discovered knowledge and intelligence
  • Knowledge and intelligence actionability enhancement
  • Inconsistencies between discovered and existing knowledge