SCC-PG: Building an Integrative Community Platform to Alleviate the Workforce Aging Crisis

SCC-PG：构建一体化社区平台，缓解劳动力老龄化危机

• 批准号: 1952225
• 负责人: Shangping Ren
• 金额: $ 15万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952225&HistoricalAwards=false
• 关键词: SCC; PG; Building; Integrative; Community

The United States is in the midst of a workforce aging crisis. According to labor force projections, workers aged 55 and older will not only be the fastest growing segment of the labor market in the coming decade, but will also make up 25.2% (compared to 13.1% in 2000) of the total workforce by 2020, with approximately 41 million workers will be over the age of 55 by 2024. Although automation has become widespread in many industries, some workplaces such as utility industries still rely heavily on individuals to perform critical tasks. These individuals have been performing critical tasks over a long period of time and have accumulated crucial experience and domain knowledge. This intellectual capital not only improves productivity and efficacy under normal conditions, but more importantly, it enables quick identification of anomalies and swift action to unexpected events and situations. We may soon lose the accrued essential knowledge as skilled workers retire. This planning project will serve as a vital starting point to build a community platform to alleviate the fast-growing workforce aging crisis that builds upon integrative research spanning technical and social dimensions.The main objective of this project is to form a task force to develop a road map to combat the imminent workforce aging challenge in collaboration with the San Diego community. In particular, we will form a team of researchers (both in social sciences and STEM), community and industry partners (including skilled workers, members of management teams, industrial and community representatives, human resource representatives, and policy makers) to develop a set of computer technologies and tools to accurately capture and retain the critical experiences and non-conventional knowledge. This team will work to effectively transfer that knowledge to the new workforce, and develop a set of policies, protocols, and processes to alleviate and/or even prevent future workforce aging crisis. This project includes multidisciplinary efforts from computer science and engineering, policy making and social science, and participation of community agencies, stakeholders, and skilled workers. This integrative approach will lead to a sustainable technology transfer platform for the community and stakeholders to address the workforce aging issue, and a smarter and more connected community where critical experiences and unconventional knowledge can be accurately retained and effectively transferred to the next generation workforce.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.

美国正处于劳动力老龄化危机之中。根据劳动力预测，55岁及以上的工人不仅是未来十年劳动力市场增长最快的部分，而且到2020年将占总劳动力的25.2%（2000年为13.1%），到2024年将有大约4100万工人超过55岁。尽管自动化在许多行业已经普及，但一些工作场所，如公用事业行业，仍然严重依赖个人来执行关键任务。这些人在很长一段时间内一直在执行关键任务，并积累了重要的经验和领域知识。这种智力资本不仅可以提高正常情况下的生产力和功效，更重要的是，它可以快速识别异常情况并对意外事件和情况采取迅速行动。随着技术工人的退休，我们可能很快就会失去积累的基本知识。这个规划项目将作为一个重要的起点，建立一个社区平台，以缓解快速增长的劳动力老龄化危机，建立在跨技术和社会层面的综合研究的基础上。这个项目的主要目标是成立一个工作组，与圣地亚哥社区合作制定一个路线图，以应对迫在眉睫的劳动力老龄化挑战。特别是，我们将组建一个由研究人员（包括社会科学和STEM），社区和行业合作伙伴（包括技术工人，管理团队成员，行业和社区代表，人力资源代表和政策制定者）组成的团队，以开发一套计算机技术和工具，以准确捕捉和保留关键经验和非传统知识。该团队将致力于有效地将这些知识转移给新的劳动力，并制定一套政策，协议和流程，以减轻和/或甚至防止未来的劳动力老龄化危机。该项目包括计算机科学和工程，政策制定和社会科学的多学科努力，以及社区机构，利益相关者和技术工人的参与。这种综合方法将为社区和利益相关者提供一个可持续的技术转让平台，以解决劳动力老龄化问题，以及一个更聪明、联系更紧密的社区，在那里关键的经验和非传统的知识可以准确地保留并有效地转移给下一代劳动力。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识产权进行评估来支持的。优点和更广泛的影响审查标准。

项目成果

DOWELL: Diversity-Induced Optimally Weighted Ensemble Learner for Predictive Maintenance of Industrial Internet of Things Devices

• DOI: 10.1109/jiot.2021.3097269
• 发表时间: 2022-02
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Onat Güngör;T. Rosing;Baris Aksanli

Using Event Log Timing Information to Assist Process Scenario Discoveries

使用事件日志计时信息来协助流程场景发现

• DOI: 10.1109/aike48582.2020.00017
• 发表时间: 2020
• 期刊: 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE
• 作者: Zhang, Zhenyu;Guo, Chunhui;Peng, Wenyu;Ren, Shangping
• 通讯作者: Ren, Shangping

STEWART: STacking Ensemble for White-Box AdversaRial Attacks Towards more resilient data-driven predictive maintenance

• DOI: 10.1016/j.compind.2022.103660
• 发表时间: 2022-09
• 期刊: Comput. Ind.
• 作者: Onat Güngör;Tajana Simunic;Baris Aksanli

PMDG: Privacy for Multi-perspective Process Mining Through Data Generalization

PMDG：通过数据泛化实现多视角流程挖掘的隐私

• 发表时间: 2023
• 期刊: 35th International Conference on Advanced Information Systems Engineering (CAiSE
• 作者: Hildebrant, Ryan;Fahrenkrog-Petersen, Stephan A.;Weidlich, Matthias;Ren, Shangping
• 通讯作者: Ren, Shangping

Process scenario discovery from event logs based on activity and timing information

根据活动和计时信息从事件日志中发现流程场景

• DOI: 10.1016/j.sysarc.2022.102435
• 发表时间: 2022
• 期刊: Journal of systems architecture
• 影响因子: 4.5
• 作者: Zhenyu, Zhang;Caleb, Johnson;Nalini, Venkatasubramanian;Shangping, Ren
• 通讯作者: Shangping, Ren