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Ubiquitous Data Mining is the process of analysing data emanating from distributed and heterogeneous sources in the form of a continuous stream with mobile and/or embedded devices. Topics include but are not restricted to:
  • Adaptive Data Mining
  • Distributed Data Mining
  • Distributed Data Streams
  • Grid Data Mining
  • Learning in Ubiquitous environments
  • Learning from Sensor Networks
  • Learning from Social Networks
  • Visualization Techniques for UDM
  • Incremental and On-line Learning Algorithms
  • Single-Pass and Scalable Algorithms
  • Learning in distributed neural network systems;
  • Real-Time and Real-World Applications
  • Resource-aware UDM
  • Theoretical frameworks for UDM