CIC-DM 2016 - 2016 International Workshop on Collaborative Internet Computing for Disaster Management

The ability to collect, analyze, and integrate data from large number of diverse data sources has offered unprecedented opportunities to study human behaviors and their relationship to various types of systems and services. Mobile phone data and the content generated by hundreds of millions of users on social media such as Twitter, or Facebook, present continuous data streams of human social activities, and offer a unique chance to understand the structure and dynamics of social and information behavior in various situations. The goal of this workshop is to bring together researchers and practitioners working in the related areas of big data, internet computing and crisis information management to meet the growing challenges in efficient disaster management. We aim to foster a productive collaboration between computer/information scientists, public policy and urban planners, government officials, and other interested participants to discuss issues and challenges related to disaster management. This includes theoretical, methodological, ethical, and political questions in regard to the study of large-scale emergency related data and intelligent systems. A particular objective of the CIC-DM'16 is to bridge the gap between the methods of scalable data management, data mining, and the smart applications to improve emergency responses. We aim to provide a platform for the exchange of ideas, identification of important and challenging problems, and discovery of possible synergies. Our hope is that this workshop will spur vigorous discussions and encourage collaboration between the various disciplines resulting in joint projects and grant submissions.
Topics of Interest
This workshop welcomes submissions on various research topics within the contexts of emergency and crisis informatics and management, include but are not limited to the following:
Data analytics Human/social computing Data management Emergent research issues
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