CAREER: Real-Time Crowd-Oriented Search and Computation Systems

职业：面向人群的实时搜索和计算系统

• 批准号: 1149383
• 负责人: James Caverlee
• 金额: $ 50.33万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-15 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149383&HistoricalAwards=false
• 关键词: CAREER; Real; Time; Crowd; Oriented

While long-lived communities have been one of the key organizing principles of Web-based systems, there is widespread evidence of highly-dynamic, ad-hoc crowd formation in emerging real-time socio-computational systems. These crowds are dynamically formed and potentially short-lived, often with only implicit signals of their formation and evolution. The goal of this research project is to develop the framework, algorithms, and systems for lightweight crowd-oriented search and computation so that stakeholders can distill high-quality information from bursty social systems and actively engage with the crowds generating this information. First, the project provides the foundation for crowd-oriented search through new algorithmic advances for distributed crowd indexing and in an investigation of the design principles impacting crowd-oriented search. Next, the project develops self-tuning methods for assessing crowd quality, even with huge demands on efficiency and in the presence of limited evidence of crowd quality. Finally, the project explores methods for "closing the loop" in crowd-oriented search, so that crowds may become part of in situ human-computational systems. The education and outreach efforts of the project are tightly linked to the research goals through leadership workshops, enhancements to the curricula, direct research training, and engagement with emergency response experts and major companies. Distilling high-quality information from bursty social systems and actively engaging with the crowds generating this information will result in improved real-time decision-making, impacting a wide range of stakeholders from areas such as epidemiology, law enforcement, government, finance, politics, among many others. Further information can be found on the project web page: http://faculty.cse.tamu.edu/caverlee/csc/.

虽然长寿的社区一直是基于Web的系统的关键组织原则之一，有广泛的证据表明，在新兴的实时社会计算系统的高度动态，特设人群的形成。这些群体是动态形成的，可能是短暂的，往往只有其形成和演变的隐含信号。该研究项目的目标是开发轻量级面向人群的搜索和计算的框架，算法和系统，以便利益相关者可以从突发的社会系统中提取高质量的信息，并积极参与产生这些信息的人群。首先，该项目通过分布式人群索引的新算法进步和对影响面向人群搜索的设计原则的调查，为面向人群的搜索提供了基础。接下来，该项目开发了用于评估人群质量的自调整方法，即使对效率的要求很高，并且人群质量的证据有限。最后，该项目探索了在面向人群的搜索中“闭合循环”的方法，以便人群可以成为现场人类计算系统的一部分。该项目的教育和外联工作与研究目标密切相关，办法是举办领导能力讲习班、改进课程、直接研究培训以及与应急专家和大公司接触。从突发的社会系统中提取高质量的信息，并积极与产生这些信息的人群互动，将改善实时决策，影响来自流行病学、执法、政府、金融、政治等领域的广泛利益相关者。更多信息可在项目网页上找到：http://faculty.cse.tamu.edu/caverlee/csc/。