Abstract
Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.
Highlights
This study investigates a unified batch approach to spatial proximity (SP) queries in dynamic spatial networks
incremental network expansion (INE) [3] and Range network expansion (RNE) [3] were used to evaluate the nearest neighbor (NN) and RN queries, respectively, for the dynamic spatial networks because INE and RNE are based on network expansion similar to Dijkstra’s algorithm, which is well-suited to dynamic spatial networks
Note that the proposed unified batch algorithm (UBA) was not sensitive to | Q|, unlike SEQ, which means that the effectiveness of batch processing in UBA increased as | Q| increased
Summary
This study investigates a unified batch approach to spatial proximity (SP) queries in dynamic spatial networks. This study assumes that both the query points and data points run freely within the spatial network and that spatial segments often change their weights In this example, q1NN and q3NN find the data points closest to q1NN and q3NN , respectively, and q2RN finds data points within a query distance (e.g., 4 km) to q2RN . A simple solution sequentially retrieves data points that satisfy the condition of each SP query in Q This simple solution involves unnecessary network traversal, which can result in prohibitively high computational costs when a large number of SP queries reach the LBS server during peak hours [3,12,13,14,15]. UBA (3) Grouping nearby query points into query clusters (4) Retrieving candidate data points for each query cluster (5) Evaluating each query using candidate data points
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