Continuous distance-based skyline queries in road networks

In recent years, the research community has introduced various methods for processing skyline queries in road networks. A skyline query retrieves the skyline points that are not dominated by others in terms of static and dynamic attributes (i.e., the road distance). This paper addresses the issue of efficiently processing continuous skyline queries in road networks. Two novel and important distance-based skyline queries are presented, namely, the continuous  dε-skylinedε-skylinequery   (Cdε-SQCdε-SQ) and the continuous k nearest neighbor-skyline query (Cknn-SQ  ). A grid index is first designed to effectively manage the information of data objects and then two algorithms are proposed, the Cdε-SQCdε-SQalgorithm   and the Cdε-SQ+Cdε-SQ+algorithm  , which are combined with the grid index to answer the Cdε-SQCdε-SQ. Similarly, the Cknn-SQ algorithm and the Cknn-SQ+algorithm are developed to efficiently process the Cknn-SQ. Extensive experiments using real road network datasets demonstrate the effectiveness and the efficiency of the proposed algorithms.

► We focus on processing the continuous skyline query in road networks.
► We design a grid index to effectively manage the information of data objects.
► We propose several algorithms combined with the grid index to answer the skyline queries.
► We conduct extensive experiments to demonstrate the efficiency of the proposed algorithms.