Spatial and Spatio-Temporal Aggregation

空间和时空聚合

• 批准号: 0100436
• 负责人: Bongki Moon
• 金额: $ 34.43万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0100436&HistoricalAwards=false
• 关键词: Spatial; Spatio; Temporal; Aggregation

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.

聚集计算是昂贵的，特别是当它涉及到时变的属性。计算聚集的任务变得更具挑战性的空间和时空数据库，因为聚集值持有的空间和时间范围必须计算。例如，多属性图像通常是地球观测系统（EOS）环境中的查询的结果，其检索在满足查询的时空边界的像素上入射的所有测量。多属性影像的每一个像素都有数个相关的值，本计画发展一套时空聚合的计算技术。虽然在时间聚合方面已经做了大量的工作，但对如何评估空间聚合却知之甚少。对已有的时间聚集和空间连接算法进行了推广，建立了计算空间聚集的高效算法，并进一步推广到时空聚集。此外，可扩展的技术将通过并行化的聚合算法的共享无架构。该项目小组包括美国地质调查局死亡谷区域水流系统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，该试验场被提议作为高放射性核废料的储存库。该研究成果将直接影响到地球观测系统、地籍数据库、大气数据库、水文数据库等大规模时空数据库的应用。