Within Skyline Query Processing in Dynamic Road Networks

Abstract

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.

Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part II: Impact, Comparison, and Future Developments

“…Nowadays, there are plenty of exploratory studies on how best to model and visualize 2D/3D geospatial information (such as maps, images, and 3D models) on virtual globes [64][65][66]. Visualizing and analyzing geospatial objects in four dimensions (x, y, z, and time), however, is still a relatively new field [4][5][6][7][8][9][10][11][12][13][14][15][67][68][69][70].…”

Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part I: Overview and Key Issues

“…It is well known that the Earth is three-dimensional and rapidly changing over time. With the recent technological advances in geospatial information acquisition, such as global navigation satellite systems (GNSS), remote sensing (RS), mobile computing, and sensor networks, the number of time-dynamic geospatial objects and their applications in various disciplines are steadily increasing [56][57][58][59][60]. Prominent examples of time-dynamic geospatial objects are satellites, aircraft, cars, ships, wild animals, mobile subscribers, meteorological disasters, forest fires, land cover, snow coverage in mountains, and tectonic plates [61][62][63][64].…”