Call for Paper
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