Scalable processing of continuous K-nearest neighbor queries with uncertain velocity

Abstract

Continuous K-nearest neighbor (C KNN) query is an important type of spatio-temporal queries. Given a time interval [ t s , t e ] and a moving query object q, a C KNN query is to find the K-nearest neighbors ( KNNs) of q at each time instant within [ t s , t e ]. In this paper, we focus on the issue of scalable processing of C KNN queries over moving objects with uncertain velocity. Due to the large amount of C KNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the C KNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree.