CAREER: Fast Query Support for Emerging Spatial Database Applications

职业：新兴空间数据库应用程序的快速查询支持

• 批准号: 0347600
• 负责人: Donghui Zhang
• 金额: $ 50.6万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347600&HistoricalAwards=false
• 关键词: CAREER; Fast; Query; Support; Emerging

Spatial databases and Geographic Information Systems (GIS) have many applications. Examples include environmental systems, corporate decision-support systems, travel arrangement systems, etc. Typically, such applications have large volumes of data, with a critical need for fast query response. This project examines efficient query processing in spatial databases. In particular, the research focuses on the selection query, the aggregation query and the proximity query. The selection query finds the objects in a user-specified region. For instance, find objects in the Washington, DC area. This project examines novel techniques that are more efficient than existing approaches. The aggregation query computes some aggregate information in a user-specified region, for example: find the total number of restaurants in a given region, find the total precipitation of rainfall in New York State over the past year, etc. Here enumerating the actual objects is not required for answering the query. This project designs algorithms with sub-linear query cost. The proximity query finds interesting results based on the closeness of objects, for example: find a hotel/library pair which are the closest to each other, find a location for a potential supermarket which will be close to maximum number of residents, find an apartment whose weighted total distance to a supermarket and a subway station is minimal. The project will advance the spatial database technology, and will have an impact on many fields that deal with spatial data. When systems in such fields are equipped with more expressive query features and faster query processing algorithms, the end users will benefit from the research results. The project also has a strong educational focus, including a modern curriculum which is based on not only providing the students with textbook knowledge and hands-on practice, but involving them in research. The project Web site http://www.ccs.neu.edu/home/donghui/research/CAREER_grant will be used to disseminate the published papers, and the system integration and the experimental results.

空间数据库和地理信息系统（GIS）有许多应用。例如环境系统、企业决策支持系统、出行安排系统等。通常，此类应用程序具有大量数据，迫切需要快速查询响应。该项目研究空间数据库中的高效查询处理。具体来说，研究重点是选择查询、聚合查询和邻近查询。选择查询查找用户指定区域中的对象。例如，查找华盛顿特区地区的对象。该项目研究比现有方法更有效的新技术。聚合查询计算用户指定区域内的一些聚合信息，例如：查找给定区域内的餐馆总数、查找纽约州过去一年的降雨量总降水量等。这里枚举实际对象不需要回答查询。该项目设计了具有次线性查询成本的算法。邻近查询根据对象的接近程度找到有趣的结果，例如：查找彼此最接近的酒店/图书馆对、查找接近最大居民数量的潜在超市的位置、查找公寓其到超市和地铁站的加权总距离最小。该项目将推进空间数据库技术，并对处理空间数据的许多领域产生影响。当这些领域的系统配备更具表现力的查询功能和更快的查询处理算法时，最终用户将从研究成果中受益。该项目还具有很强的教育重点，包括现代课程，该课程不仅为学生提供课本知识和实践实践，而且让他们参与研究。该项目网站http://www.ccs.neu.edu/home/donghui/research/CAREER_grant将用于传播已发表的论文、系统集成和实验结果。