Geometric Techniques for Multidimensional Databases

多维数据的存储和检索对于许多科学、地理、工程和业务应用程序都是必要的。传统的数据库二级存储索引技术（如b树和哈希表）不适合处理多维数据，因为数据访问一次只集中在一个维度上执行，可能会检索到大量无关的数据。该项目的主要目标是探索和开发用于单机和并行机架构的高效加载、检索和更新大型磁盘驻留多维数据的技术。同时考虑了点数据和区域数据的存储和处理。数据库社区已经独立开发了许多基于磁盘的多维索引方案，而没有应用可能显著提高加载和检索性能的相关领域的结果。因此，本项目中使用的方法牢固地建立在计算几何领域的基础上，这一学科已经产生了许多有效处理内存驻留多维数据的技术。推广和改进这些技术以有效地处理磁盘驻留数据是本研究的重要组成部分。此外，还考虑使用并行性为非常大的数据集提供改进的负载和查询响应时间。由此产生的索引技术将使更高级别的数据库体系结构能够提供对未来非常大的多维数据集的有效访问，从而使数据库技术能够跟上科学和商业对它的整体需求。

Storage and retrieval of multidimensional data is necessary for many scientific, geographic, engineering, and business applications. Traditional database secondary storage indexing techniques such as B-trees and hash tables are not suitable for dealing with multidimensional data, as data access is performed by concentrating on only one-dimension at a time and large quantities of extraneous data may be retrieved. The main goal of this project is to explore and develop techniques for efficient loading, retrieval, and update of large disk-resident multidimensional data for both single machine and parallel machine architectures. Storage and processing of both point and region data are considered. The database community has independently developed a number of disk-based multi-dimensional indexing schemes, without applying results from relevant fields that may significantly improve loading and retrieval performance. Accordingly, the approach used in this project is firmly grounded within the realm of computational geometry, a discipline that has produced numerous techniques for the efficient processing of memory-resident multidimensional data. The generalization and improvement of these techniques to handle efficiently disk-resident data is an important part of this research. Additionally, the use of parallelism to provide improved load and query response times for very large data sets is also considered. The resulting indexing techniques will enable higher level database architectures to provide efficient access to the very large multi-dimensional datasets of the future, thus enabling database technology to keep up with demands placed upon it from science and business as a whole.