2017 ACM International Conference On Computing and Data Analysis (ICCDA 2017)

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