RII Track-4: Harnessing Big Event Data with Heterogeneous Feature: Intelligent Food-Borne Outbreak Investigations and Beyond

RII Track-4：利用具有异构特征的大事件数据：智能食源性疫情调查及其他

• 批准号: 1929091
• 负责人: Xiao Liu
• 金额: $ 23.82万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929091&HistoricalAwards=false
• 关键词: RII; Track; Harnessing; Big; Event

This award will advance the Nation's food safety, cyber security and economic welfare by innovating new statistical learning methodologies that enhance the critical capabilities of harnessing big event data with heterogeneous feature information. The penetration of Big Data technologies into interdisciplinary domains has led to an explosive growth of large-scale recurrent event data. Examples include, but not limited to, recurrent outbreaks of food-borne diseases from urban blocks in major cities, repeated cyber-attacks against vital infrastructures, recurring disasters or extreme weather events at critical geo-locations, and failures experienced by repairable engineering systems under dynamic operating-environmental conditions. This award will investigate the integration of modern additive-tree-based statistical learning approaches and classical point processes models for the modeling, prediction and optimization of large-scale recurrent event processes. Through the collaboration with the Industrial and Applied Genomics team at IBM Almaden Research Center (San Jose, CA), this project will test and validate the capabilities of the proposed methodologies in accelerating food-borne outbreak investigations using real data. The project also includes activities to benefit the PI's home institution, including nation-wide competitive intern programs that are currently rare for students at the PI's jurisdiction, especially for underrepresented communities. Integrating the research outcomes into the Data Analytics Minor program at the PI's home institution will nurture a pool of next-generation data scientists and engineers for the northwest Arkansas.This project will investigate a set of new additive-tree-based statistical learning methods to enable effective modeling, prediction and optimization of large-scale event processes with heterogeneous feature information. Based on the actual use cases provided by IBM research teams, this project will extensively investigate and demonstrate the advantages of a promising idea that integrates modern additive-tree-based methods and classical statistical models for stochastic point processes. This project consists of three Research Tasks (RT) during the PI's visit to IBM Almaden Research Center. RT1 and RT2 will propose two algorithms, RF-E and Boost-E, for modeling large-scale event data with both static and dynamic features. The two algorithms are deeply rooted in the framework of Random Forests and Gradient Boosting, respectively. RT3 will perform comprehensive model testing and validation on intelligent food-borne outbreaks investigation, with the critical support from IBM Research. Due to the interdisciplinary nature of the proposed research, the developed methodologies will lead to innovative solutions for a spectrum of event analytics applications arising from cross-disciplinary domains, including food safety, cyber security, reliability, online retail, transportation safety, disaster and extreme weather events.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.

该奖项将通过创新新的统计学习方法，提高利用具有异构特征信息的大事件数据的关键能力，来促进国家的食品安全，网络安全和经济福利。大数据技术向跨学科领域的渗透导致了大规模重复事件数据的爆炸式增长。例子包括但不限于，在主要城市的城市街区反复爆发食源性疾病，对重要基础设施的反复网络攻击，在关键地理位置反复发生的灾害或极端天气事件，以及可修复的工程系统在动态操作环境条件下遇到的故障。该奖项将研究现代基于加法树的统计学习方法和经典点过程模型的集成，用于大规模经常性事件过程的建模，预测和优化。通过与IBM Almaden研究中心（加利福尼亚州圣何塞）的工业和应用基因组学团队的合作，该项目将使用真实的数据测试和验证拟议方法在加速食源性疫情调查方面的能力。该项目还包括有利于PI所在机构的活动，包括全国范围内的竞争性实习计划，这些计划目前对PI管辖范围内的学生来说很少见，特别是对代表性不足的社区来说。将研究成果整合到PI所在机构的Data Analytics Minor计划中，将为阿肯色州西北部培养一批下一代数据科学家和工程师。该项目将研究一套新的基于加性树的统计学习方法，以实现对具有异构特征信息的大规模事件过程的有效建模、预测和优化。基于IBM研究团队提供的实际用例，该项目将广泛研究和展示一个有前途的想法的优势，该想法将现代基于加法树的方法和随机点过程的经典统计模型相结合。该项目由PI访问IBM Almaden研究中心期间的三项研究任务（RT）组成。RT 1和RT 2将提出两种算法，RF-E和Boost-E，用于对具有静态和动态特征的大规模事件数据进行建模。这两种算法分别深深扎根于随机森林和梯度提升的框架中。RT 3将在IBM Research的关键支持下，对智能食源性疫情调查进行全面的模型测试和验证。由于拟议研究的跨学科性质，所开发的方法将为跨学科领域的事件分析应用提供创新解决方案，包括食品安全，网络安全，可靠性，在线零售，运输安全，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Optimal Sensor Placement for Atmospheric Inverse Modelling

大气反演模拟的最佳传感器放置

• DOI: 10.1109/bigdata55660.2022.10020757
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Liu, Xinchao;Yeo, Kyongmin;Klein, Levente;Hwang, Youngdeok;Phan, Dzung;Liu, Xiao
• 通讯作者: Liu, Xiao

Boost-R: Gradient boosted trees for recurrence data

Boost-R：用于重复数据的梯度提升树

• DOI: 10.1080/00224065.2021.1948373
• 发表时间: 2021
• 期刊: Journal of Quality Technology
• 影响因子: 2.5
• 作者: Liu, Xiao;Pan, Rong
• 通讯作者: Pan, Rong

Analysis of Large Heterogeneous Repairable System Reliability Data with Static System Attributes and Dynamic Sensor Measurement in Big Data Environment

大数据环境下具有静态系统属性和动态传感器测量的大型异构可修复系统可靠性数据分析

• DOI: 10.1080/00401706.2019.1609584
• 发表时间: 2019
• 期刊: Technometrics
• 影响因子: 2.5
• 作者: Liu, Xiao;Pan, Rong
• 通讯作者: Pan, Rong