50.
In this paper, we study the problem of continuous monitoring of reverse
k nearest neighbors queries in Euclidean space as well as in spatial networks. Existing techniques are sensitive toward objects
and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We
present a framework for continuous reverse
k nearest neighbor (R
kNN) queries by assigning each object and query with a
safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions.
This significantly improves the computation cost. As a byproduct, our framework also reduces the communication cost in client–server
architectures because an object does not report its location to the server unless it leaves its safe region or the server
sends a location update request. We also conduct a rigid cost analysis for our Euclidean space R
kNN algorithm. We show that our techniques can also be applied to answer
bichromatic R
kNN queries in Euclidean space as well as in spatial networks. Furthermore, we show that our techniques can be extended for
the spatial networks that are represented by directed graphs. The extensive experiments demonstrate that our techniques outperform
the existing techniques by an order of magnitude in terms of computation cost and communication cost.
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