ITR: Development of a Data Engine for Grid-Enabled Analytical Computing

ITR：开发用于支持网格的分析计算的数据引擎

• 批准号: 0635365
• 负责人: Ratko Orlandic
• 金额: $ 11.66万
• 依托单位: University of Illinois at Springfield
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-16 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0635365&HistoricalAwards=false
• 关键词: ITR; Development; Data; Engine; Grid

The objective of this project is to support the Data Grid infrastructure by developing new and original techniques for efficient storage, retrieval, and analysis of complex scientific data. The main focus is on the development of a highly scalable data engine geared toward the needs of analytical computing in Data Grid environments. However, keys to the realization of the project are the advances in the areas of indexing and clustering data in multi-dimensional spaces.While the main goal of analytical computing in Data Grid environments is to facilitate hypothesis formulation or to test the validity of a postulated scientific model, its primary method is usually that of data clustering. Since typical analytical tasks also rely on ad-hoc data exploration, any data engine for Grid-enabled analytical computing must support an integrated set of different retrieval and clustering techniques. The data engine developed in this project will feature: an efficient and scalable indexing technique for data in high-dimensional spaces, which will include a practical solution for handling data with missing information; a new and original access method for similarity searching in multi-dimensional spaces; and an original technique for clustering large volumes of multi-dimensional data, which will require no dimensionality reduction.

该项目的目标是通过开发新的和原始的技术来支持数据网格基础设施，以有效地存储，检索和分析复杂的科学数据。主要的重点是一个高度可扩展的数据引擎的发展，面向分析计算的需要，在数据网格环境。然而，该项目的实现的关键是在多维空间中的索引和聚类数据的领域的进步。虽然在数据网格环境中的分析计算的主要目标是促进假设公式化或测试假设的科学模型的有效性，其主要方法通常是数据聚类。由于典型的分析任务也依赖于ad-hoc数据探索，因此任何支持网格的分析计算的数据引擎都必须支持一组集成的不同检索和聚类技术。在这个项目中开发的数据引擎将具有以下功能：一个高效的、可扩展的高维空间数据索引技术，其中包括一个处理缺失信息数据的实用解决方案;一个新的、原始的多维空间相似性搜索访问方法;以及一个用于聚类大量多维数据的原始技术，它不需要降维。