| Summary: | 碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 95 === One of the most important queries in spatio-temporal databases that aims at managing moving objects efficiently is the continuous K-Nearest Neighbor (CKNN) query.
Given a future time interval [ts, te] and a moving query object q, a CKNN query is to retrieve the K-Nearest Neighbor (KNN) of q from ts to te.
In this paper, we investigate how to process a CKNN query efficiently.
Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed speed and a fixed direction, by allowing that the speed and direction of the object can vary within a known range.
Allowing objects move with uncertain speed and direction makes our work more suitable for the real-life applications.
Due to the introduction of this uncertainty on the speed and direction of each object, processing a CKNN becomes much more complicated.
We propose an approach to remedy the shortcomings of the previous work so as to efficiently process CKNN query.
In addition, we also develop an efficient pruning strategy operated by the support of a data-partition index to achieve low I/O and CPU costs.
Comprehensive experiments demonstrate the efficiency and effectiveness of our proposed approach.
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