In conjunction with The IEEE
International Conference on Collaboration and Internet Computing (CIC 2016)
October 31th, 2016, Pittsburgh, PA, USA
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:
- 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
Talk & Paper Submission
We provide different submission formats: full papers, short papers, and poster/demo papers. We encourage submissions which present early stages of cutting-edge research and development in the form of short or poster papers. The format is the standard two-column IEEE proceeding format. Accepted papers will appear in the Proceedings. Additional information about formatting and style files are available online.
Please submit your contribution via the EasyChair.
Louise Comfort Professor of Public and International Affairs Director, Center for Disaster Management, University of Pittsburgh Dr. Comfort's primary research interests are in decision making under conditions of uncertainty and rapid change, interactions among technical and organizational systems under stress, and uses of information technology to develop decision support systems for managers operating under urgent conditions. She uses methods of network analysis, system dynamics modeling, and focuses on the analysis of large-scale, sociotechnical, complex adaptive systems. Her current research focuses on The Dynamics of Risk: Changing Technologies, Complex Systems, and Collective Action. Dr. Comfort holds a Bachelor of Arts degree in Political Science and Philosophy from Macalester College, St. Paul, Minnesota, a Master of Arts degree in Political Science from the University of California, Berkeley, and a Ph.D. degree in Political Science from Yale University. She is a Fellow of the U. S. National Academy of Public Administration, and has been a Visiting Scholar at five international universities. She has engaged in field studies following twenty-two earthquake disasters in fourteen countries, including the EERI reconnaissance study of September 30, 2009 Padang Earthquake, Indonesia, National Science Foundation (NSF) RAPID response study of the January 12, 2010 Haiti Earthquake, a recovery study of the March 11, 2011 Tohoku Earthquake, Japan, NSF RAPID response study of the May 13, 2014 Soma, Manisa, Turkey Mine Fire, and an NSF RAPID study of response and recovery from the 25 April and 12 May 2015 Earthquakes in, Nepal.Details coming soon!
Yu-Ru Lin Email is an assistant professor at the School of Information Sciences, University of Pittsburgh. Her research interests include human mobility, social and political network dynamics, and computational social science. She has developed computational approaches for mining and visualizing large-scale, time-varying, heterogeneous, multi-relational, and semi-structured data. Her current research focuses on extracting system-level features from big data sets, including social media data and anonymized cellphone records, for studying human and social dynamics, particularly under exogenous events such as emergencies and media events. Her work has appeared in prestigious scientific venues including WWW, SIGKDD, InfoVis, ACM TKDD, ACM TOMCCAP, IEEEP and PLoS ONE.
Vladimir Zadorozhny Email is an Associate Professor of Information Sciences at the University of Pittsburgh School of Information Sciences. He received his Ph.D. in 1993 from the Institute for Problems of Informatics, Russian Academy of Sciences in Moscow. Before coming to USA he was a Principal Research Scientist in the Institute of System Programming, Russian Academy of Sciences. Since 1998 he worked as a Research Associate in the University of Maryland Institute for Advanced Computer Studies at College Park. He joined University of Pittsburgh in 2001. His research interests include information integration and fusion, complex adaptive systems and crowdsourcing, query optimization in resource-constrained distributed environments, and scalable architectures for wide-area environments with heterogeneous information servers. His research has been supported by NSF, EU and Norwegian Research Council. He is a recipient of Fulbright Scholarship for 2014-2015. Vladimir has received several best paper awards and has chaired and served on program committees of multiple Database and Distributed Computing Conferences and Workshops.
Ole-Christoffer Granmo Email is Director of the Centre for Integrated Emergency Management (CIEM) at the University of Agder, Norway. He obtained his M.Sc. in 1999 and the Ph.D. degree in 2004, both from the University of Oslo, Norway. His research interests include Intelligent Systems, Stochastic Modelling and Inference, Machine Learning, Pattern Recognition, Reinforcement Learning, Distributed Computing, Computational Linguistics, and Surveillance and Monitoring. Within these areas of research, Dr. Granmo has written more than 100 refereed journal and conference publications. He also serves on the Editorial Board of Disaster Communications, specializing within artificial intelligence support for crisis preparedness and management.
Coordination and Publicity
Xidao Wen Email is a Ph.D student at the School of Information Sciences, University of Pittsburgh. His research interests include data science, urban informatics, and disaster informatics. His current work focuses on studying the impact of disasters (e.g., terrorist attacks, massive violence, earthquakes, etc.) in the urban environment, with particular interests in building computational tools/frameworks to uncover the shifts and dynamics from the emotions, human mobilities, and topics surrounding the affected population. He is now affiliated with PICSO Lab, and Personalized Adaptive Web Systems Lab (PAWS).