Processing continuous K-nearest skyline query with uncertainty in spatio-temporal databases

Abstract

Continuous K-nearest skyline query (CKNSQ) is an important query in the spatio-temporal databases. Given a query time interval [ts,te] and a moving query object q, a CKNSQ is to retrieve the K-nearest skyline points of q at each time instant within [ts,te]. Different from the previous works, in this paper we devote to overcoming the specific assumptions that each object is static in road networks and has the certain dimensional values. We focus on processing the CKNSQ over moving objects with uncertain dimensional values in Euclidean space and the velocity of each object (including the query object) varies within a known range. As the uncertainty is involved, such a query is called the continuous possible K-nearest skyline query (CPKNSQ). We first discuss the difficulties raised by the uncertainty of moving objects and then propose the CPKNSQ algorithm operated with a data-partitioning index, the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPKNSQ. Moreover, we design a probability-based model to quantify the possibility of each object being the query result. Finally, extensive experiments using the synthetic and real datasets demonstrate the effectiveness and the efficiency of the proposed approaches.