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

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。主要焦点是开发高度可伸缩的数据引擎，以满足数据网格环境中分析计算的需求。然而，实现该项目的关键是在多维空间的索引和聚类数据领域的进展。虽然数据网格环境中分析计算的主要目标是促进假设的制定或测试假设科学模型的有效性，但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特别的数据探索，任何用于支持网格的分析计算的数据引擎都必须支持一组不同的检索和聚类技术。在这个项目中开发的数据引擎将具有以下特点：高维空间数据的高效和可扩展索引技术，其中将包括处理缺少信息的数据的实用解决方案；一种新颖的多维空间相似性检索方法一种原始的技术，用于聚类大量的多维数据，这将不需要降维。

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.