A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

Abstract

Abstract:The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR) query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET) structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency.

Differentiating geographic movement described in text documents

“…These research efforts provide a baseline for analysis for other research that utilizes trajectories by characterizing trajectories and movement data with these attributes and taxonomies. These frameworks have been used to organize research analysis into traffic interchange patterns (Zeng, Fu, Arisona, & Qu, 2013), detection of anomalies in traffic (Orellana, Wachowicz, Andrienko, & Andrienko, 2009), identifying key segments of trajectories (Ferrero, Alvares, Zalewski, & Bogorny, 2018), and to construct database queries specific to moving objects (Zhang, Lu, & Chen, 2016), to name just a few. Although these frameworks are for trajectories, they overlap with movement statements in that they are about geographic movement.…”

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

“…Here, Incremental Euclidean Restriction (IER) [18] uses an R-tree to process objects on networks and the experimental results demonstrate that the performance is worse than incremental network expansion (INE) [18] and Voronoi-based range search algorithm (VRS) [17] . To address the dead space problem, a number of works address spatial queries on road networks, i.e., range queries [18,28], continuous range queries [29], k-nearest neighbor [19,28], continuous k-nearest neighbor [30], and shortest path queries [31,32]. These works improve the performance for specific queries, but these methods do not consider the data distribution.…”

“…These works improve the performance for specific queries, but these methods do not consider the data distribution. Specifically, works such as [29,30] view each edge as a unit, and a road network is partitioned into an equal number of edges. An example here is the G-tree [19] and G*-tree [28], which is a hierarchical tree structure.…”

“…Those algorithms partition vertices of the graph, thus loosing ("cutting") edges connecting vertices in different partitions. To achieve lossless graph partitioning, we leverage the concept of a line graph [29], which represents edges of the original graph as vertices that are connected by edges if they (the edges in the original graph) share a common vertex. In the line graph, we can then use traditional graph partitioning algorithms [37,38] for lossless partitioning.…”

Current Trends and Challenges in Location-Based Services

“…Their experiment showed that the proposed algorithm significantly reduced the query processing time when compared with the period solution. Zhang, Lu, and Chen [13] addressed the problem of continuous range queries of moving objects in networks. They presented a line-graph-based algorithm, which was characterized by a novel graph-based expansion tree structure to monitor queries in an incremental way.…”