CSR-PDOS-Content-Searchable Storage for Feature-Rich Data

CSR-PDOS-功能丰富的数据的内容可搜索存储

基本信息

批准号：
0509447
负责人：
Kai Li
金额：
$ 90万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2005
资助国家：
美国
起止时间：
2005-07-01 至 2009-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509447&HistoricalAwards=false
关键词：
CSR PDOS Content Searchable Storage

项目摘要

Storage capacity and data volume have been doubling every 18 months during the pasttwo decades. A key challenging issue in building next-generation storage systems is tomanage massive amounts of feature-rich (non-text) data, which has dominated theincreasing volume of digital information. Comparing noisy, feature-rich data requires fast similarity match instead of exact match, and thus exploring such data requires similaritysearch instead of exact search. Current file systems are designed for named text files;they do not have mechanisms to manage feature-rich data. To date, there is no practicalstorage system with the ability to do similarity search for noisy, high-dimensional dataand there is no index engine design for efficient similarity search. This researchaddresses this problem by studying how to design and implement a content-addressableand -searchable storage (CASS) system to manage and explore diverse feature-rich data.The system includes a built-in similarity search engine for general-purpose, noisy, highdimensionalmetadata using compact data structures and novel indexing methods. Theresearch will also develop segmentation methods and feature extraction methods foraudio, image and genomic data, and develop similarity search benchmarks and toevaluate the CASS system.This research will advance knowledge and understanding in the area of storage systemdesigns such as data structures, mechanisms, and APIs for managing, searching andexploring noisy, high-dimensional feature-rich data. The research will accelerate thedevelopment of next-generation storage systems which will revolutionize how to access,search, explore and manage massive amounts of feature-rich data in many disciplines.

在过去的几十年中，存储容量和数据量每18个月增加一倍。构建下一代存储系统的一个关键挑战性问题是大量功能丰富（非文本）数据，该数据占据了数字信息的数量。比较嘈杂，功能丰富的数据需要快速相似性匹配而不是确切的匹配，因此探索此类数据需要相似性搜索而不是精确的搜索。当前的文件系统是为命名文本文件设计的；它们没有用于管理功能丰富数据的机制。迄今为止，还没有实用性系统，无法进行相似性搜索以获取嘈杂，高维数据，并且没有索引发动机设计可进行有效的相似性搜索。这项研究通过研究如何设计和实施内容 - 搜索存储（CASS）系统来管理和探索各种功能丰富的数据。该系统包括使用紧凑的数据结构和新颖的索引方法的内置相似性搜索引擎。 Theresearch还将开发细分方法和功能提取方法，图像和基因组数据，并开发相似性搜索基准并为CASS系统进行评估。这项研究将提高存储系统设计领域的知识和理解，例如数据结构，机制和API，用于管理，搜索，搜索和exploring Noisisy noisisy noisisy，高清功能符合数据。这项研究将加速下一代存储系统的开发，这些系统将彻底改变如何访问，搜索，探索和管理许多学科的大量功能丰富数据。