Efficient Processing of All Nearest Neighbor Queries in Dynamic Road Networks

Abstract

The increasing trend of GPS-enabled smartphones has led to the tremendous usage of Location-Based Service applications. In the past few years, a significant amount of studies have been conducted to process All nearest neighbor (ANN) queries. An ANN query on a road network extracts and returns all the closest data objects for all query objects. Most of the existing studies on ANN queries are performed either in Euclidean space or static road networks. Moreover, combining the nearest neighbor query and join operation is an expensive procedure because it requires computing the distance between each pair of query objects and data objects. This study considers the problem of processing the ANN queries on a dynamic road network where the weight, i.e., the traveling distance and time varies due to various traffic conditions. To address this problem, a shared execution-based approach called standard clustered loop (SCL) is proposed that allows efficient processing of ANN queries on a dynamic road network. The key concept behind the shared execution technique is to exploit the coherence property of road networks by clustering objects that share common paths and processing the cluster as a single path. In an empirical study, the SCL method achieves significantly better performance than competitive methods and efficiently reduces the computational cost to process ANN queries in various problem settings.

ParSCL: A Parallel and Distributed Framework to Process All Nearest Neighbor Queries on a Road Network

“…An array table holds the nearestneighbor result for all nodes. Bhandari et al [18] proposed an algorithm named SCL based on a shared-execution technique aiming to reduce the run-time by reducing redundant query searches. However, these techniques aim to support efficient ANN processing in a centralized manner.…”

“…It then creates an RDD from the gathered information. Subsequently, the RDDs are distributed to each executor which is responsible for computing the local ANN utilizing the approach presented in [18]. The local computed results are returned to the master node.…”

“…The distribution of the data and query objects followed a centroid distribution unless otherwise specified. To evaluate the effectiveness of the proposed scheme, we utilized the INE [20] algorithm and a centralized algorithm SCL [18] to compute the ANN queries across all n query objects. It is worth noting that the SCL algorithm was originally developed for centralized computing and lacks the necessary framework to support parallel execution.…”

“…Albeit, efficient algorithms for processing ANN exist for centralized data-bases [14]- [18], it is vital to provide a more efficient, distributed, and parallel framework that scales well with the increasing number of queries, where the computation is shared among multiple available servers. The main goal of this study is to design and implement a robust distributed and parallel-processing ANN architecture that provides high scalability and performance for spatial query processing.…”