Mining large-scale high utility patterns in vehicular ad hoc network environments

One well-known type of mobile ad hoc network is known as a vehicular ad hoc network (VANET). The functions of such a network are integrated into a new generation of wireless networks for vehicles, which has established a robust self-organizing network that exists between roadside units and mobile vehicles. In this article, we research the comfort applications in VANET with a new proposed algorithm, EHUM, short form for efficient high utility itemset mining, to mine patterns of the more popular Points of Interest (POIs). This algorithm is based on the traditional high-utility itemset mining (HUIM) algorithm, and we propose a more reasonable pruning strategy. Concurrently, for solving the problem of excessive data volume in VANET, we applied this algorithm to the MapReduce architecture used for improving the feasibility in practical applications. Our in-depth work in this article culminates with some experimental results that clearly show that our proposed algorithm can perform well to mine the POIs pattern in a big data data set and shows great performance in a Hadoop computing cluster.