Continuous possible K-nearest skyline query in euclidean spaces

Continuous K-nearest skyline query (CKNSQ) is an important type of the spatio-temporal queries. 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, our work devotes to overcoming the past assumption that each object is static with certain dimensional values and located in road networks. In this paper, 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. Such a query is called the continuous possible K-nearest skyline query (CPKNSQ). We first discuss the difficulties raised by the uncertainty of object and then propose the CPKNSQ algorithm operated with a data partitioning index, called the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPKNSQ.