Important: To submit your new paper(s), please simply click 2014 International 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 from DDDM2007 to DDDM2013, DDDM2014 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.