SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases

  • Authors:

  • Xiaopeng Xiong;Mohamed F. Mokbel;Walid G. Aref

  • Affiliations:

  • Purdue University;Purdue University;Purdue University

  • Venue:
  • ICDE '05 Proceedings of the 21st International Conference on Data Engineering
  • Year:
  • 2005

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Abstract

Location-aware environments are characterized by a large number of objects and a large number of continuous queries. Both the objects and continuous queries may change their locations over time. In this paper, we focus on continuous k-nearest neighbor queries (CKNN, for short). We present a new algorithm, termed SEA-CNN, for answering continuously a collection of concurrent CKNN queries. SEA-CNN has two important features: incremental evaluation and shared execution. SEA-CNN achieves bothefficiency and scalability in the presence of a set of concurrent queries. Furthermore, SEA-CNN does not make any assumptions about the movement of objects, e.g., the objects velocities and shapes of trajectories, or about the mutability of the objects and/or the queries, i.e., moving or stationary queries issued on moving or stationary objects. We provide theoretical analysis of SEA-CNN with respect to the execution costs, memory requirements and effects of tunable parameters. Comprehensive experimentation shows that SEA-CNN is highly scalable and is more efficient in terms of both I/O and CPU costs in comparison to other R-tree-based CKNN techniques.