BigDataFr recommends: Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data
Keywords: Nearest neighbor, Trajectory join, Big trajectory data, MapReduce.

Abstract

[…] Trajectory data are prevalent in systems that monitor the locations of moving objects. In a location-based service, for instance, the positions of vehicles are continuously monitored through GPS; the trajectory of each vehicle describes its movement history. We study joins on two sets of trajectories, generated by two sets M and R of moving objects.

For each entity in M , a join returns its k nearest neighbors from R. We examine how this query can be evaluated in cloud environments. This problem is not trivial, due to the complexity of the trajectory, and the fact that both the spatial and temporal dimensions of the data have to be handled. To facilitate this operation, we propose a parallel solution framework based on MapReduce. We also develop a novel bounding technique, which enables trajectories to be pruned in parallel. […]

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By Yixiang Fang ¹, Reynold Cheng¹, Wenbin Tang¹, Silviu Maniu², Xuan Yang¹
Source: hal-archives-ouvertes.fr

1 LI – HKU – The University of Hong Kong
2 LI – LaHDAK – Données et Connaissances Massives et Hétérogènes
LRI – Laboratoire de Recherche en Informatique