{"title":"利用Dijkstra算法在空间网络数据库中逐步找到k个最近邻","authors":"Victor Teixeira de Almeida, R. H. Güting","doi":"10.1145/1141277.1141291","DOIUrl":null,"url":null,"abstract":"One of the most important kinds of queries in Spatial Network Databases (SNDB) to support Location-Based Services (LBS) is the k-Nearest Neighbors (k-NN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc., the k-NN query returns the k points of interest closest to the query point. The network distance is used in such a query instead of the Euclidean distance. Dijkstra's algorithm is a well known solution to this problem. In this paper, we propose a storage schema with a set of index structures to support an efficient execution of a slightly modified version of the Dijkstra's algorithm. We show in an experimental evaluation with generated data sets that our proposal is more efficient than the state-of-the-art solution to this problem.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Using Dijkstra's algorithm to incrementally find the k-Nearest Neighbors in spatial network databases\",\"authors\":\"Victor Teixeira de Almeida, R. H. Güting\",\"doi\":\"10.1145/1141277.1141291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most important kinds of queries in Spatial Network Databases (SNDB) to support Location-Based Services (LBS) is the k-Nearest Neighbors (k-NN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc., the k-NN query returns the k points of interest closest to the query point. The network distance is used in such a query instead of the Euclidean distance. Dijkstra's algorithm is a well known solution to this problem. In this paper, we propose a storage schema with a set of index structures to support an efficient execution of a slightly modified version of the Dijkstra's algorithm. We show in an experimental evaluation with generated data sets that our proposal is more efficient than the state-of-the-art solution to this problem.\",\"PeriodicalId\":269830,\"journal\":{\"name\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1141277.1141291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using Dijkstra's algorithm to incrementally find the k-Nearest Neighbors in spatial network databases
One of the most important kinds of queries in Spatial Network Databases (SNDB) to support Location-Based Services (LBS) is the k-Nearest Neighbors (k-NN) query. Given a point in a network, e.g. a location of a car on a road network, and a set of points of interests, e.g. hotels, gas stations, etc., the k-NN query returns the k points of interest closest to the query point. The network distance is used in such a query instead of the Euclidean distance. Dijkstra's algorithm is a well known solution to this problem. In this paper, we propose a storage schema with a set of index structures to support an efficient execution of a slightly modified version of the Dijkstra's algorithm. We show in an experimental evaluation with generated data sets that our proposal is more efficient than the state-of-the-art solution to this problem.