Spatial and Spatio-Temporal Aggregation

聚合计算的代价很高，尤其是在涉及时变属性的情况下。对于空间和时空数据库来说，计算聚合体的任务变得更具挑战性，因为必须计算聚合值所在的空间和时间范围。例如，多属性图像通常是地球观测系统(EOS)环境中查询的结果，其检索满足查询时空边界的像素上入射的所有测量值。多属性图像的每个像素都有几个与其相关联的值。该项目开发了一套计算时空聚集体的技术。虽然在时间聚集方面已经做了大量的工作，但人们对如何评估空间聚集知之甚少。对已有的时间聚集和空间连接算法进行了推广，建立了高效的空间聚集计算算法，并对这些算法进行了进一步推广以适应时空聚集。此外，将通过在无共享体系结构上并行化聚合算法来开发可伸缩技术。项目组包括美国地质调查局关于USGS死亡谷区域流动系统(DVRFS)项目的一名水文学家，该项目正在调查内华达州试验场的地下水流动，该试验场被提议作为高放核废料的储存库。该研究成果对EOS、地籍数据库、大气数据库、水文数据库等大型时空数据库应用有着直接的影响。

Aggregate computation is expensive, especially when time-varying attributes are involved in it. The task of computing aggregates becomes more challenging for spatial and spatio-temporal databases, as the spatial and temporal extent over which the aggregate value holds must be computed. For example, multi-attribute images are often the result of a query in the Earth Observing System (EOS) environments, which retrieves all of the measurements incident on the pixels that satisfy the spatio-temporal bounds of the query. Each pixel of multi-attribute images has several values associated with it. This project develops a suite of techniques for computing spatio-temporal aggregates. While there has been significant work done in temporal aggregation, little is known about how to evaluate spatial aggregates. The existing temporal aggregation and spatial join algorithms are generalized to create efficient algorithms for computing spatial aggregates, and then further generalized these algorithms to accommodate spatio-temporal aggregates. In addition, scalable techniques will be developed by parallelizing the aggregation algorithms on a shared-nothing architecture. The project team includes a hydrologists at the United States Geological Survey on the USGS Death Valley Regional Flow System (DVRFS) Project, which is investigating ground-water flow in the Nevada Test Site, proposed as a repository for high-level nuclear waste. The results from this research has a direct impact on many large-scale spatio-temporal database applications such as EOS, cadastral databases, atmospheric databases and hydrologic databases.