Making Sense at Scale with Algorithms, Machines, and People

通过算法、机器和人员大规模地发挥意义

• 批准号: 1139158
• 负责人: Ion Stoica
• 金额: $ 600万
• 依托单位: University of California-Berkeley
• 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: 2012-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1139158&HistoricalAwards=false
• 关键词: Making; Sense; Scale; Algorithms; Machines

Making Sense at Scale with Algorithms, Machines, and PeopleUniversity of California, BerkeleyThe world is increasingly awash in data. As more and more human activities move on line, and as a growing array of connected devices become integral part of daily life, the amount and diversity of data being generated continues to explode. According to one estimate, more than a Zettabyte (one billion terabytes) of new information was created in 2010 alone, with the rate of new information increasing by roughly 60% annually. This data takes many forms: free-form tweets, text messages, blogs and documents; structured streams produced by computers, sensors and scientific instruments; and media such as images and video.Buried in this flood of data are the keys to solving huge societal problems, for improving productivity and efficiency, for creating new economic opportunities, and for unlocking new discoveries in medicine, science and the humanities. However, raw data alone is not sufficient; we can only make sense of our world by turning this data into knowledge and insight. This challenge, known as the Big Data problem, cannot be solved by the straightforward application of current data analytics technology due to the sheer volume and diversity of information. Rather, to solve it requires throwing away old preconceptions about data management and breaking down many of the traditional boundaries in and around Computer Science and related disciplines. The Algorithms, Machines, and People (AMP) expedition at the University of California, Berkeley is addressing this challenge head-on. AMP is a collaboration of researchers with a wide range of data-related expertise, committed to working together to create a new data analytics paradigm. AMP will produce fundamental innovations in and a deep integration of three very different types of computational resources: 1. Algorithms: new machine-learning and analysis methods that can operate at large scale and can give flexible tradeoffs between timeliness, accuracy, and cost. 2. Machines: systems infrastructure that allows programmers to easily harness the power of scalable cloud and cluster computing for making sense of data. 3. People: crowdsourcing human activity and intelligence to create hybrid human/computer solutions to problems not solvable by today's automated data analysis technologies alone.AMP research will be guided and evaluated through close collaboration with domain experts in key societal applications including: cancer genomics and personalized medicine, large-scale sensing for traffic prediction and environmental monitoring, urban planning, and network security. Advances pioneered by the project will be made widely available through the development of the Berkeley Data Analysis System (BDAS), an open source software platform that seamlessly blends Algorithm, Machine and People resources to solve big data problems.For more information visit http://amplab.cs.berkeley.edu

加州大学伯克利分校世界正日益被数据所淹没。随着越来越多的人类活动在网上进行，随着越来越多的连接设备成为日常生活中不可或缺的一部分，生成的数据的数量和多样性继续呈爆炸式增长。根据一项估计，仅在2010年就产生了超过1泽字节（10亿太字节）的新信息，新信息的速度每年大约增长60%。这些数据有多种形式：格式自由的推文、短信、博客和文档；由计算机、传感器和科学仪器产生的结构化流；以及图像和视频等媒体。在这些数据洪流中，隐藏着解决巨大社会问题、提高生产力和效率、创造新的经济机会，以及在医学、科学和人文科学领域开启新发现的关键。然而，仅凭原始数据是不够的；我们只能通过将这些数据转化为知识和洞察力来理解我们的世界。这一挑战被称为大数据问题，由于信息的庞大数量和多样性，不能通过当前数据分析技术的直接应用来解决。相反，要解决这个问题，需要抛弃关于数据管理的旧观念，打破计算机科学和相关学科内外的许多传统界限。加州大学伯克利分校（University of California, Berkeley）的算法、机器和人（AMP）探险队正在直面这一挑战。AMP是具有广泛数据相关专业知识的研究人员的合作，致力于共同创造一种新的数据分析范式。AMP将在三种非常不同类型的计算资源方面产生根本性的创新和深度整合：算法：新的机器学习和分析方法，可以大规模运行，可以灵活地在及时性、准确性和成本之间进行权衡。2. 机器：系统基础设施，它允许程序员轻松地利用可伸缩云和集群计算的力量来理解数据。3. 人：众包人类活动和智能，以创造混合的人/计算机解决方案，以解决当今自动化数据分析技术无法解决的问题。AMP研究将通过与关键社会应用领域专家的密切合作来指导和评估，包括：癌症基因组学和个性化医疗，交通预测和环境监测的大规模传感，城市规划和网络安全。该项目率先取得的进展将通过伯克利数据分析系统（BDAS）的开发得到广泛应用。BDAS是一个开源软件平台，可以无缝地融合算法、机器和人员资源，以解决大数据问题。更多信息请访问http://amplab.cs.berkeley.edu