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

Provided by: Purdue Federal Credit Union
Topic: Data Management
Format: PDF
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, the authors focus on continuous k-nearest neighbor queries (CKNN, for short). They 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 both efficiency 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.

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