CAREER: Coping with the Data Deluge - Algorithms, Human Aspects and Applications

职业：应对数据洪流 - 算法、人性化和应用

• 批准号: 0953413
• 负责人: Andreas Krause
• 金额: $ 50.64万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953413&HistoricalAwards=false
• 关键词: CAREER; Coping; Data; Deluge; Algorithms

Massive volumes of data are now generated in social, scientific, business and military applications. However, access to the data is limited, due to computational, bandwidth, power limitations, as well as human constraints such as attention and privacy. A fundamental problem is thus to obtain most useful information at minimum cost. Most existing techniques are heuristics without guarantees, do not scale or cannot cope with dynamic, distributed phenomena that change over time. In addition, existing algorithms typically disregard human aspects of the information gathering task. This research (1) develops principled algorithms for optimized information gathering in distributed, uncertain, dynamic domains, (2) studies human aspects of optimized information gathering, considering attention, perception and privacy as fundamental constraints and (3) pursues novel, real-world applications. To accomplish these goals, fundamentally new techniques in combinatorial optimization, probabilistic reasoning and decision theory are developed. The approaches are unified in the mathematical framework of submodular function optimization. In addition to developing theoretically well-founded, rigorous approaches, an important part of the research is evaluation on interdisciplinary, real world applications in social computing, scientific data analysis and stochastic optimization in sustainability. These applications of optimized information gathering have the potential to enable new classes of systems and services for science and society. Impact is achieved through collaboration with partners from industry (Microsoft Corporation) and governmental facilities (USGS and JPL), as well as by broad dissemination of the results through an integrated education plan, tutorials, shared data and open-source software, and interdisciplinary collaborations.

现在，在社会、科学、商业和军事应用中产生了海量数据。然而，由于计算、带宽、功率限制以及注意力和隐私等人为限制，对数据的访问受到限制。因此，一个根本的问题是以最低的成本获得最有用的信息。大多数现有的技术都是启发式的，没有保证，不能扩展，或者不能处理随时间变化的动态、分布式现象。此外，现有的算法通常忽略了信息收集任务中的人的方面。本研究(1)开发了分布式、不确定、动态领域中的优化信息收集的原则性算法；(2)研究了优化信息收集的人的方面，将注意力、感知和隐私作为基本约束；(3)追求新颖的、真实世界的应用。为了实现这些目标，在组合优化、概率推理和决策理论方面出现了根本性的新技术。这些方法统一在子模函数优化的数学框架中。除了开发理论上有充分依据的、严谨的方法外，研究的一个重要部分是对社会计算、科学数据分析和可持续发展中的随机优化的跨学科、现实世界应用的评估。这些优化信息收集的应用有可能为科学和社会提供新的系统和服务类别。通过与工业界(微软公司)和政府机构(美国地质勘探局和喷气推进实验室)的合作伙伴合作，以及通过综合教育计划、教程、共享数据和开放源码软件以及跨学科协作，广泛传播成果，实现了影响。