Call for Papers (Download)

Topics are interested but not limited to:

    Foundations

  • Mathematical, probabilistic and statistical models and theories
  • Machine learning theories, models and systems
  • Knowledge discovery theories, models and systems
  • Manifold and metric learning
  • Deep learning
  • Scalable analysis and learning
  • Non-iidness learning
  • Heterogeneous data/information integration
  • Data pre-processing, sampling and reduction
  • Dimensionality reduction
  • Feature selection, transformation and construction
  • Large scale optimization
  • High performance computing for data analytics
  • Architecture, management and process for data science


    • Data analytics, machine learning and knowledge discovery
  • Learning for streaming data
  • Learning for structured and relational data
  • Latent semantics and insight learning
  • Mining multi-source and mixed-source information
  • Mixed-type and structure data analytics
  • Cross-media data analytics
  • Big data visualization, modeling and analytics
  • Multimedia/stream/text/visual analytics
  • Relation, coupling, link and graph mining
  • Personalization analytics and learning
  • Web/online/social/network mining and learning
  • Structure/group/community/network mining
  • Cloud computing and service data analysis


    • Storage, retrieval and search
  • Data warehouses, cloud architectures
  • Large-scale databases
  • Information and knowledge retrieval, and semantic search
  • Web/social/databases query and search
  • Personalized search and recommendation
  • Human-machine interaction and interfaces
  • Crowdsourcing and collective intelligence

 

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