Nearest group queries

Dongxiang Zhang, Chee-Yong Chan, Kian-Lee Tan
2013 Proceedings of the 25th International Conference on Scientific and Statistical Database Management - SSDBM  
k nearest neighbor (kNN) search is an important problem in a vast number of applications, including clustering, pattern recognition, image retrieval and recommendation systems. It finds k elements from a data source D that are closest to a given query point q in a metric space. In this paper, we extend kNN query to retrieve closest elements from multiple data sources. This new type of query is named k nearest group (kNG) query, which finds k groups of elements that are closest to q with each
more » ... up containing one object from each data source. kNG query is useful in many location based services. To efficiently process kNG queries, we propose a baseline algorithm using R-tree as well as an improved version using Hilbert R-tree. We also study a variant of kNG query, named kNG Join, which is analagous to kNN Join. Given a set of query points Q, kNG Join returns k nearest groups for each point in Q. Such a query is useful in publish/subscribe systems to find matching items for a collection of subscribers. A comprehensive performance study was conducted on both synthetic and real datasets and the experimental results show that Hilbert R-tree achieves significantly better performance than R-tree in answering both kNG query and kNG Join.
doi:10.1145/2484838.2484866 dblp:conf/ssdbm/ZhangCT13 fatcat:qk5vo4juwjfudl2tzyjp6rk6ey