$\sf {SIMkNN}$: A Scalable Method for in-MemorykNN Search over Moving Objects in Road Networks.

IEEE Transactions on Knowledge and Data Engineering(2018)

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摘要
Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split...
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关键词
Indexes,Roads,Search problems,Query processing,Artificial neural networks,Real-time systems,Time factors
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