CCF-BSF: CIF: Small: Collaborative Research: Coding and Information - Theoretic Aspects of Local Data Recovery

CCF-BSF：CIF：小型：协作研究：编码和信息 - 本地数据恢复的理论方面

基本信息

批准号：
1618603
负责人：
Alexander Barg
金额：
$ 24.98万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618603&HistoricalAwards=false
关键词：
CCF BSF CIF Small Collaborative

项目摘要

This project studies fundamental problems in data coding that can improve the efficiency of distributed storage systems by increasing data reliability and availability while reducing storage overhead compared to existing industry standards. The results of this research can benefit storage applications ranging from financial, scientific monitoring, and signal processing to social networks and sharing platforms. The new combinatorial, coding, and information theoretic tools developed in this project will be incorporated in course curricula in the respective institutions of the principal investigators.Data coding with locality, the focus of this project, is a rapidly developing area of coding theory that was initially motivated by applications in distributed storage, and has links to many areas of network science (e.g., index coding and network coding) as well as to computer science. This project advances the theory and practice of data coding with local recovery by investigating broad implications of the locality constraint in coding problems. These include studying new error-correcting code families and their decoding, fundamental limitations on the code parameters and capacity bounds under the requirements of local data recovery. The newly designed coding schemes developed in this project will be validated through implementation and evaluation in simulated computer environment, aiming at enhanced performance of data coding in current industry solutions.

该项目研究数据编码中的基本问题，通过提高数据可靠性和可用性，同时与现有行业标准相比减少存储开销，从而提高分布式存储系统的效率。这项研究的结果可以使从金融、科学监测、信号处理到社交网络和共享平台等存储应用受益。该项目中开发的新组合、编码和信息论工具将纳入主要研究者各自机构的课程课程中。该项目的重点是局部数据编码，是编码理论中一个快速发展的领域。最初是由分布式存储中的应用程序激发的，并且与网络科学的许多领域（例如索引编码和网络编码）以及计算机科学有联系。该项目通过研究编码问题中局部性约束的广泛影响，推进了具有本地恢复的数据编码的理论和实践。其中包括研究新的纠错码系列及其解码、本地数据恢复要求下的代码参数和容量界限的基本限制。该项目开发的新设计编码方案将通过在模拟计算机环境中的实施和评估进行验证，旨在增强现有行业解决方案中数据编码的性能。