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

通过加利福尼亚州的算法，机器和人民助理，伯克利世界越来越多地吸引了数据。 随着越来越多的人类活动在线移动，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，所生成的数据的数量和多样性仍在爆炸。 根据一项估计，仅2010年创建了超过Zettabyte（十亿TB）的新信息，每年的新信息速度大约增加60％。 该数据采用多种形式：自由形式的推文，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；图像和视频等媒体在这些数据泛滥中浮动是解决巨大的社会问题，提高生产力和效率，创造新的经济机会以及解锁医学，科学和人文科学方面的新发现的关键。 但是，仅原始数据就不够。我们只能通过将这些数据变成知识和洞察力来了解我们的世界。 由于信息的数量和多样性，当前数据分析技术的直接应用无法通过直接应用，这一挑战被称为大数据问题。 相反，要解决它需要抛弃有关数据管理的旧概要，并分解了计算机科学和相关学科的许多传统界限。加州大学伯克利分校的算法，机器和人（AMP）探险正在正面解决这一挑战。 AMP是研究人员的合作，具有广泛的与数据相关的专业知识，致力于共同创建新的数据分析范式。 AMP将产生三种非常不同类型的计算资源的基本创新，并深入整合：1。算法：可以大规模运行的新机器学习和分析方法，可以在及时性，准确性和成本之间提供灵活的权衡。 2.机器：允许程序员可以轻松利用可扩展云和群集计算的功率的系统基础架构来理解数据。 3.人：众包人类的活动和智力，以创建混合人类/计算机解决方案，以解决当今的自动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的近距离合作进行指导和评估，包括：癌症基因组学和个性化医学，个性化医学，用于预测和环境的大型预测和环境计划，脉动监控和网络安全和网络。 该项目开创的进步将通过开发伯克利数据分析系统（BDAS）的开发，这是一个开源软件平台，该平台无缝地融合了算法，机器和人们的资源来解决大数据问题。