Distance-Based Continuous Skylines On Geo-Textual Data

Various kinds of data from real applications are usually associated with geographic information as well as textual descriptions. Existing methods mostly consider static query points not supporting moving context. In this paper, we study distance-based continuous skyline queries on geo-textual data. That is, given a set of geo-textual data and a distance constraint, the skyline queries continuously return the objects that are not dominated by others when the query point is moving within the distance constraint. We only consider distance and text relevance dimensions and two algorithms are introduced to calculate initial skyline results and continuous skylines efficiently. Also, grid file indexes are exploited to accelerate continuous skyline query processing. Experimental results on real datasets demonstrate the efficiency and effectiveness of our proposed methods.