Extracting emergency events from big data

Measurement of relevance and user activities through emergency information retrieval in social streams

Identifying misinformation during emergencies and crisis events

Evaluation framework for the emergency mining algorithms

Scalable or real-time architecture for large-scale emergency information processing, mining and visualization

Emergency social and information structure pattern discovery and predictive modeling

Social network analysis and spatiotemporal analysis for crisis management

Collective sense-making in crisis events

Scalable collaborative graph data processing and streaming data processing for rare events

Human computer interfaces for emergency data mining and crowdsourcing

Visual analytics for crisis informatics

Large-scale collective intelligence for emergency data integration and data fusion; fusion of social communication features, metadata, user generated content, and social context within the emergency situations

Large-scale process monitoring for handling high data rates during emergencies

Security and privacy management for emergency information processing

Collaborative Big Data storage and management in the cloud for emergency management

Collaborative Big Data reliability assessment for crisis informatics

Challenges for collaboration in Big Data emergency management and data utilization

New technologies (e.g., mobile applications) for mining and deploying emergency information

Probabilistic computing in disasters

Reinforcement learning for autonomous systems

Any-time algorithms for large-scale in-situ reinforcement learning

Deep learning for complex and fast evolving decision scenarios

Resilient machine learning for planning & coordination in hostile environments