Nearest group queries

  • Authors:

  • Dongxiang Zhang;Chee-Yong Chan;Kian-Lee Tan

  • Affiliations:

  • National University of Singapore, Singapore;National University of Singapore, Singapore;National University of Singapore, Singapore

  • Venue:
  • Proceedings of the 25th International Conference on Scientific and Statistical Database Management
  • Year:
  • 2013

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Abstract

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 group 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.