Humanitarian & Social Informatics Lab, GMU
NSF III Small Collaborative Research Award # 1815459 & 1814958 Project: III: Small: Collaborative Research: Summarizing Heterogeneous Crowdsourced & Web Streams Using Uncertain Concept Graphs (Collaborative Grant Page)
Sponsor: National Science Foundation
Team: George Mason University (GMU), Vanderbilt University (VU)
Project & GMU Principal Investigator: Dr. Hemant Purohit
GMU Co-Principal Investigator: Dr. Huzefa Rangwala
VU Principal Investigator: Dr. Abhishek Dubey
VU Co-Principal Investigator: Dr. Gautam Biswas

About


Ubiquitous access to mobile and web technologies enables the public to share valuable information about their surroundings anywhere and anytime. For example, during an emergency or crisis people report needs from affected areas via social media as an alternative to the traditional 911 calls. This can be valuable information for a range of emergency service officials. However, the utilization of this data poses several computational challenges as it is generated in real time, is heterogeneous, highly unstructured, redundant, and sometimes unreliable. The project investigates new summarization approaches to handle noisy, unstructured data streams from multiple web sources in real time while accounting for the possibility of untrustworthy information, so that they can be fed into decision support systems of public services in a structured and machine-readable format. In addition, the project develops and validates robust decision support systems for allocating critical resources to needed areas based on the structured summary reports. The evaluation plan includes collaboration with emergency responders and the communities they serve. The broader impacts of this research include the design of a generic methodology to extract, integrate, and summarize structured information from big data streams on the web for helping public services of future smart cities. The research team plans to share simulated datasets with an open source system for real-time decision support during emergency response exercises. This can assist in workforce training and also, help design novel educational projects of data science for social good.

Formally, this research project investigates the theories behind a novel knowledge representation called Uncertain Concept Graph. The graph contains heterogeneous nodes based on key concepts of an application domain (e.g., regions, incidents, and information sources during a disaster). The graph has heterogeneous edges connecting these concept nodes, based on the inference of concept relationships using the extracted information from data streams (e.g., Twitter and news sources). The structure of the graph evolves over time and both nodes and edges can be added, deleted, or updated. An equivalent Bayesian Network is derived from the Uncertain Concept Graph describing the dependencies between the events captured in the graph at a given time instance. Based on the relationship edges in a graph state and the constructed Bayesian Network, an action recommendation system is created to support an application domain task (e.g., dispatching ambulance resources to incident-specific regions). To ensure robustness, this project develops and validates a novel anomaly identification and diagnosis approach using mode similarity to assess the correctness of current state of concept nodes and their relationships in the Uncertain Concept Graph at any time. The research team uses historical datasets of recent disasters to construct the graph and develop a demo system for domain evaluation, in order to recommend actions in emergency response for the city emergency services. The investigators are including the lessons learned and methodologies developed in their respective course curricula.

Selected Publications


  • Purohit, Hemant and Kanagasabai, Rajaraman and Deshpande, Nikhil. "Towards Next Generation Knowledge Graphs for Disaster Management," The 13th IEEE International Conference on Semantic Computing (ICSC), 2019. doi:10.1109/ICOSC.2019.8665638

  • Pandey, Rahul and Castillo, Carlos and Purohit, Hemant. "Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing," The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2019.

  • Purohit, Hemant and Castillo, Carlos and Imran, Muhammad and Pandey, Rahul. "Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers," The 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2018. doi:10.1109/WI.2018.00-88

  • Pandey, Rahul and Bahl, Gaurav and Purohit, Hemant. "EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services," The 16th International Conference on Information Systems for Crisis Response And Management (ISCRAM), 2019.

  • Purohit, Hemant and Dubrow, Samantha and Bannan, Brenda. "Designing a Multimodal Analytics System to Improve Emergency Response Training," The 21st International Conference on Human-Computer Interaction, 2019.

  • Purohit, Hemant and Nannapaneni, Saideep and Dubey, Abhishek and Karuna, Prakruthi and Biswas, Gautam. "Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph," 2018 IEEE International Science of Smart City Operations and Platforms Engineering in Partnership with Global City Teams Challenge (SCOPE-GCTC), 2018. doi:10.1109/SCOPE-GCTC.2018.00012

  • Samal, Chinmaya and Dubey, Abhishek and Ratliff, Lillian. "Mobilytics-Gym: A simulation framework for analyzing urban mobility decision strategies," The 2019 IEEE International Conference on Smart Computing (SMARTCOMP), 2019. doi:10.1109/SMARTCOMP.2019.00064

  • Mukhopadhyay, Ayan and Pettet, Geoffrey and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy. "An online decision-theoretic pipeline for responder dispatch," The 10th ACM/IEEE International Conference on Cyber-Physical Systems, 2019. doi:10.1145/3302509.3311055

People


Faculty:
- Dr. Hemant Purohit, GMU
- Dr. Huzefa Rangwala, GMU
- Dr. Abhishek Dubey, VU
- Dr. Gautam Biswas, VU

Students:
- Jitin Krishnan (PhD research assistant, GMU)
- Rahul Pandey (PhD research assistant, GMU)
- Prashanti Maktala (MS research assistant, GMU)

Contact


If interested in this project and want to pursue PhD or MS Thesis, then you can mail at h p u r o h i t _a_t_ g m u _d_o_t_ e d u with your resume, transcripts, and any prior research papers.