SGER: Data-Distribution Properties in High Dimensional Euclidean Space and their Applications in Optimizing Multi-Media Database Accesses

SGER：高维欧几里德空间中的数据分布特性及其在优化多媒体数据库访问中的应用

• 批准号: 9910605
• 负责人: Sakti Pramanik
• 金额: $ 10万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-10-01 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9910605&HistoricalAwards=false
• 关键词: SGER; Data; Distribution; Properties; Dimensional

The characteristics of data-distribution in high dimensional Euclidean space play a fundamental role in many areas of computer science, including database systems, pattern recognition and multimedia. Several basic data distribution properties, namely the angle, the distance and the radius properties in high dimensional Euclidean Space are being investigated. Both real data sets such as feature vectors for image data and synthetic data sets with distributions such as normal, zipf, self similar and fractal are being used to analyze these properties. A component of this research is to exploit these basic properties in developing efficient indexing schemes for nearest neighbor search. Significant performance improvement in search time in the proposed approach over those currently available is expected in this research. In addition, there is a plan to apply the indexing scheme to commercial image database systems.

高维欧几里德空间中的数据分布特征在计算机科学的许多领域中发挥着基础作用，包括数据库系统、模式识别和多媒体。正在研究几个基本的数据分布特性，即高维欧几里得空间中的角度、距离和半径特性。图像数据的特征向量等真实数据集和具有正态、zipf、自相似和分形等分布的合成数据集都被用来分析这些属性。这项研究的一个组成部分是利用这些基本属性来开发用于最近邻搜索的有效索引方案。本研究预计所提出的方法比当前可用的方法在搜索时间方面有显着的性能改进。此外，还计划将该索引方案应用于商业图像数据库系统。