III: Small: Collaborative Research: Summarizing Heterogeneous Crowdsourced & Web Streams Using Uncertain Concept Graphs

III：小：协作研究：异构众包总结

• 批准号: 1814958
• 负责人: Abhishek Dubey
• 金额: $ 24万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814958&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Summarizing

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. This project innovates in two specific directions to alleviate the challenges associated with large, streaming datasets during emergencies: (1) 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. (2) 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 curriculums.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无处不在的移动和网络技术使公众能够随时随地分享有关周围环境的有价值的信息。例如，在紧急情况或危机期间，人们通过社交媒体报告受影响地区的需求，作为传统 911 电话的替代方案。这对于一系列紧急服务官员来说可能是有价值的信息。然而，这些数据的利用带来了一些计算挑战，因为它是实时生成的、异构的、高度非结构化的、冗余的，有时甚至不可靠。该项目在两个具体方向上进行创新，以缓解紧急情况下与大型流式数据集相关的挑战：（1）该项目研究新的汇总方法来实时处理来自多个网络源的噪声、非结构化数据流，同时考虑到不可信的可能性信息，以便它们能够以结构化和机器可读的格式输入公共服务的决策支持系统。 (2) 该项目开发并验证强大的决策支持系统，用于根据结构化摘要报告将关键资源分配到所需领域。评估计划包括与应急响应人员及其服务的社区的合作。这项研究的更广泛影响包括设计一种通用方法，从网络大数据流中提取、整合和总结结构化信息，以帮助未来智慧城市的公共服务。研究团队计划与开源系统共享模拟数据集，以便在应急响应演习期间提供实时决策支持。这可以帮助劳动力培训，也可以帮助设计新颖的数据科学教育项目，以造福社会。从形式上来说，这个研究项目调查了一种称为不确定概念图的新颖知识表示背后的理论。该图包含基于应用程序域的关键概念（例如灾难期间的区域、事件和信息源）的异构节点。该图具有连接这些概念节点的异构边，基于使用从数据流（例如 Twitter 和新闻源）中提取的信息进行的概念关系推断。图的结构随着时间的推移而演变，节点和边都可以添加、删除或更新。等效的贝叶斯网络源自不确定概念图，描述了给定时间实例中图中捕获的事件之间的依赖关系。基于图状态中的关系边和构建的贝叶斯网络，创建动作推荐系统来支持应用程序域任务（例如，将救护车资源调度到特定事件区域）。为了确保鲁棒性，该项目开发并验证了一种新颖的异常识别和诊断方法，使用模式相似性来随时评估不确定概念图中概念节点及其关系的当前状态的正确性。研究团队利用近期灾难的历史数据集构建图表并开发用于领域评估的演示系统，以便为城市应急服务部门的应急响应行动提供建议。调查人员将吸取的经验教训和开发的方法纳入各自的课程中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies

• DOI: 10.1109/smartcomp.2019.00064
• 发表时间: 2019-06
• 期刊: 2019 IEEE International Conference on Smart Computing (SMARTCOMP)
• 作者: Chinmaya Samal;A. Dubey;L. Ratliff

Designing Emergency Response Pipelines : Lessons and Challenges

设计应急响应管道：经验教训和挑战

• 发表时间: 2020
• 期刊: AAAI Fall Symposium Series 2020
• 作者: Mukhopadhyay, Ayan;Pettet, Geoffrey;Kochenderfer, Mykel;Dubey, Abhishek
• 通讯作者: Dubey, Abhishek

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

• DOI: 10.36001/phmconf.2019.v11i1.861
• 发表时间: 2019-09
• 期刊: Annual Conference of the PHM Society
• 作者: Sanchita Basak;Afiya Ayman;Aron Laszka;A. Dubey;Bruno P. Leao

Incident management and analysis dashboard for fire departments: ICCPS demo

消防部门的事件管理和分析仪表板：ICCPS 演示

• DOI: 10.1145/3302509.3313329
• 发表时间: 2019
• 期刊: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems
• 作者: Pettet, Geoffrey;Mukhopadhyay, Ayan;Samal, Chinmaya;Dubey, Abhishek;Vorobeychik, Yevgeniy
• 通讯作者: Vorobeychik, Yevgeniy

Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks

• DOI: 10.1109/bigdata47090.2019.9005995
• 发表时间: 2019-12
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Sanchita Basak;A. Dubey;Bruno P. Leao