期刊文献+

路网环境下的最近邻查询技术 被引量:9

Nearest Neighbor Query in Road Networks
下载PDF
导出
摘要 最近邻查询作为基于位置服务的重要支持性技术之一,引起了众多学者的广泛关注和深入研究.相对于欧式空间而言,路网环境下的最近邻查询更贴近人们的生活,有着更重要的研究意义.路网环境下庞大的数据量和复杂的数据结构,使得最近邻查询的操作代价变得非常昂贵,如何有效地提高查询效率,是研究者面临的主要挑战.对路网环境下的最近邻查询技术进行综述,分别从最近邻查询采用的索引结构和查询处理过程对现有路网环境下的最近邻查询方法进行了分析和比较,也介绍了路网环境下最近邻的变体查询技术的研究情况,最后探讨路网上最近邻查询技术未来的研究重点. Nearest neighbor query, as one of the building blocks of location-based service, has become a hot research topic in recent years. Compared with Euclidean space, road network is a more practical model in real applications; hence, nearest neighbor query in road network has received broader research efforts. In road network, tremendous data are generated along with sophisticated data structure, making nearest neighbor query computationally expensive. This poses a major challenge to spatial database community on its effort to effectively improve the query processing efficiency for nearest neighbor query. This work summarizes existing nearest neighbor query techniques in road network, and conducts analysis and comparison among them, from various perspectives including indexing structure and algorithm implementation. Additionally, several variants of nearest neighbor query are also summarized in this work. Finally, future research focus and trend for nearest neighbor query in road network are discussed.
出处 《软件学报》 EI CSCD 北大核心 2018年第3期642-662,共21页 Journal of Software
基金 国家自然科学基金(61532021 61572122) 中央高校基本科研业务费专项资金(N161606002) 辽宁省"百千万人才工程"经费~~
关键词 基于位置的服务 路网 最近邻查询 欧式距离 路网距离 location-based service road network nearest neighbor query Euclidean distance road network distance
  • 引文网络
  • 相关文献

参考文献10

二级参考文献130

  • 1郭锋,杨晨晖.连续近邻查询方法的研究[J].微计算机信息,2006,22(12S):311-314. 被引量:4
  • 2Ouri Wolfson.Moving Objects Information Management:The database challenge[C].In:Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems,London,2002,75-89.
  • 3Papadias D,Zhang J,Mamoulis N,et al.Query processing in spatial network databases[C].In:Proceedings of 29th Intl.Conf.on Very Large Data Bases,VLDB,2003,802-813.
  • 4Kolahdouzan M R,Shahabi C.Voronoi-based k nearest neighbor search for spatial network databases[C].In:Proceedings of 30th Intl.Conf.on Very Large Data Bases VLDB,2004,840-851.
  • 5Cho H J,Chung C W.An efficient and scalable approach to CNN queries in a road network[C].In Proceedings of the Intl.Conf.on Very Large Data Bases,2005,865-876.
  • 6Chon H D,Agrawal D,Abbadi A E.Range and kNN query processing for moving objects in grid model[J].MONET,2003,8(4):401-412.
  • 7Mohamed F.Mokbel,Xiaopeng Xiong,and Walid G.Aref.SINA:Scalable Incremental Processing of Continuous Queries in Spatiotemporal Databases[C].Proceedings of the Intl.Conf.on Management of Data,2004,623-634.
  • 8Yu Xiao-hui,Ken Q.Pu,Nick koudas.mointoring k Nearest neighbour queries over moving objects[C].Proceedings of the Intl.Conf.on Data Engineering,2005,631-642.
  • 9Xiong Xiao-peng,Mohamed F.Mokbel and Walid G.Aref.SEA-CNN:scalable processing of continuous K-Nearest neighbor queries in spatio-temporal databases[C].Proceedings of the Intl.Conf.on Data Engineering,2005,643-654.
  • 10Kyriakos Mouratidis,Marios Hadjieleftheriou,and Dimitris Papadias.Conceptual partitioning:an efficient method for continuous nearest neighbor monitoring[C].Proceedings of the Intl.Conf.on Management of Data,2005,634-645.

共引文献9

同被引文献39

引证文献9

二级引证文献17

相关主题

;
使用帮助 返回顶部