University - Industry Cooperative Research Programs in the Mathematical Sciences

现在可以从通信网络的高质量流量测量中获得大量数据集。对这些数据的分析揭示了可能严重影响网络性能的流量行为的意外属性。不幸的是，用于有效处理大量数据和有效分析新观察到的现象的统计和数学技术的发展，并没有跟上更新和更大的交通测量数据集被捕获的速度，也没有跟上网络本身变化的速度。计划中的研究项目直接回应了对网络流量和性能分析新方法的需求，以便有效地利用这些大型数据集，并了解新观察到的现象的影响。将任命一名博士后研究员，与at&t实验室信息科学研究中心和威斯康星大学麦迪逊分校的研究人员一起工作。研究助理将在计划的合作研究项目中提供主要的联系。该项目由MPS多学科活动办公室（OMA）和数学科学部（DMS）联合支持。

Enormous data sets are now available from high-quality traffic measurements for communications networks. Analysis of this data has revealed unexpected properties of traffic behavior which may critically affect network performance. Unfortunately, the development of statistical and mathematical techniques for efficiently dealing with this abundance of data and for effectively analyzing newly-observed phenomena has not kept up with the rate at which newer and larger data sets of traffic measurements are being captured, nor with the pace at which networks themselves change. The planned research program responds directly to the need for new approaches to network traffic and performance analysis in order to effectively exploit these large data sets and to understand the impact of the newly-observed phenomena. A postdoctoral research fellow will be appointed to work with researchers at the Information Sciences Research Center at AT&T Labs and the University of Wisconsin - Madison. The research associate will provide the primary link in the planned collaborative research program. This project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS)