Domain Driven Data Mining

Towards Domain-Driven, Actionable Knowledge Discovery and Delivery

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DDDM Workshops Series

DDDM Workshops

Since 2007, many workshops on DDDM have been held, jointly with KDD, ICDM and SDM etc. The International Workshop on Domain Driven Data Mining (DDDM) has been a premier forum for communicating the latest theoretical and practical progress on DDDM-related studies.

DDDM Workshop Scope

The DDDM Workshop series cover topics including but not limited to the following topics:

  • Domain-driven data mining/analytics/science methodology and project management 
  • Domain-driven data mining/analytics/science frameworks, system support and infrastructures
  • 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
  • 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.
  • Domain driven actionable knowledge discovery algorithms and models
  • Social data mining/analytics software
  • 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
  • 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.