{"title":"Continuous K-nearest neighbor search for moving objects","authors":"Y. Li, Jiong Yang, Jiawei Han","doi":"10.1109/SSDBM.2004.24","DOIUrl":null,"url":null,"abstract":"The paper describes a new method of continuously monitoring the k nearest neighbors of a given object in the mobile environment. Instead of monitoring all k nearest neighbors, we choose to monitor the k-th (nearest) neighbor since the necessary condition of changes in the KNN is the change of the k-th neighbor. In addition, rather than in the original space, we consider the moving objects in a transformed time-distance (TD) space, where each object is represented by a curve. A beach-line algorithm is developed to monitor the change of the k-th neighbor, which enables us to maintain the KNN incrementally. An extensive empirical study shows that the beach-line algorithm outperforms the most efficient existing algorithm by a wide margin, especially when k or n (the total number of objects) is large.","PeriodicalId":383615,"journal":{"name":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSDBM.2004.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
The paper describes a new method of continuously monitoring the k nearest neighbors of a given object in the mobile environment. Instead of monitoring all k nearest neighbors, we choose to monitor the k-th (nearest) neighbor since the necessary condition of changes in the KNN is the change of the k-th neighbor. In addition, rather than in the original space, we consider the moving objects in a transformed time-distance (TD) space, where each object is represented by a curve. A beach-line algorithm is developed to monitor the change of the k-th neighbor, which enables us to maintain the KNN incrementally. An extensive empirical study shows that the beach-line algorithm outperforms the most efficient existing algorithm by a wide margin, especially when k or n (the total number of objects) is large.