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

虽然长寿社区一直是基于Web的系统的关键组织原则之一，但在新兴的实时社会计算系统中，有广泛的证据表明高度动态的、特别的人群形成。这些群体是动态形成的，而且可能是短暂的，通常只有隐含的信号表明他们的形成和进化。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，以便利益相关者能够从突发的社会系统中提取高质量的信息，并积极参与生成这些信息的人群。首先，该项目通过分布式人群索引的新算法进展以及对影响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我调整方法，即使对效率有巨大要求，而且存在有限的人群质量证据。最后，该项目探索了在面向人群的搜索中“闭合循环”的方法，以便人群可能成为就地人类计算系统的一部分。该项目的教育和外联努力通过举办领导力讲习班、改进课程、直接进行研究培训以及与应急专家和大公司接触，与研究目标紧密联系在一起。从突如其来的社会系统中提取高质量的信息，并积极参与生成这些信息的人群，将改善实时决策，影响到流行病学、执法、政府、金融、政治等领域的广泛利益攸关方。欲了解更多信息，请访问项目网页：http://faculty.cse.tamu.edu/caverlee/csc/.

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/.