Abstract
The continuous within skyline query is an important type of location-based query, which can provide useful skyline object information for the user. Previous studies on processing the continuous within skyline query focus exclusively on a static road network, where the object attributes and the conditions of roads remain unchanged. However, in real-world applications, object attributes and road conditions inevitably vary with time, which severely limits the applicability of previous studies in practice. Therefore, in this paper, we address the issue of efficiently processing the continuous within skyline query in dynamic road networks with time-varying information. We design three elaborate data structures, the object attribute dominating matrix (OADM), the road distance sorted list (RDSL) and the skyline object expansion tree (SOET), to maintain the information of objects and the road network. Combined with OADM, RDSL and SOET, we develop an efficient algorithm, namely the within skyline object updating algorithm, to provide real-time processing of the time-varying information. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed approaches.
Highlights
With the fast advance of positioning techniques in mobile systems and the popularization of portable computers, spatio-temporal databases that aim at efficiently managing a large number of moving objects so as to support various types of location-based queries have attracted much attention in the database community [1,2,3,4]
We address the issue of efficiently processing the continuous within skyline query in dynamic road networks with time-varying information, where three types of time-varying information are taken into account
The set global within skyline objects (GWSO) is represented as a union of the within skyline objects (WSOs) of the two nodes connected by the edge e and the objects on e, where determining the WSOs of the nodes dominates the overall performance of processing the continuous within skyline query since it involves a large number of road distance computations
Summary
With the fast advance of positioning techniques in mobile systems and the popularization of portable computers (e.g., laptops, 3G mobile phones and tablet PCs), spatio-temporal databases that aim at efficiently managing a large number of moving objects so as to support various types of location-based queries have attracted much attention in the database community [1,2,3,4]. A hotel offers a 20% discount so as to attract more customers (that is, varying the “price” attribute), and a crash obstructs the road for several hours (in this case, the length of the road is changed to ∞) Such varying information about objects and roads may outdate the previous query result, so that the continuous within skyline query needs to be evaluated again. We design three elaborate data structures, the object attribute dominating matrix (OADM), the road distance sorted list (RDSL) and the skyline object expansion tree (SOET), to adequately maintain the information of objects and the road network, which can be used to facilitate the task of quickly determining which time-varying information influences the query result.
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