Using Mapreduce for Efficient Parallel Processing of Continuous K nearest Neighbors in Road Networks

Abstract

The problem of searching the continuous k Nearest Neighbor (CkNN) objects in road networks is a major challenge due to the highly dynamic nature of the road network environment. Also, the fast increasing number of moving objects poses a big challenge to the CkNN search of moving objects. In addition, it is important to deliver a valid response to the user in an optimal time while taking into account the large volume of data and the amount of changes in the characteristics of moving objects. To effectively explore the search space as well as reduce the time spent to deliver a response to the user, we propose to combine the strengths of Formal Concept Analysis (FCA), as a powerful mean of clustering the moving objects-related information, and the processing capabilities of MapReduce, as a well-known parallel programming model. The mathematical foundation of FCA allows offering an abstraction of the network based on the neighborhoods. We build the concept lattice based on the binary relations between the target points as well as their properties. The latter are collected from various sensors on the road network. We also propose a density-based road network partitioning approach and MapReduce function to distribute the search tasks. Finally, an implementation based on the Storm parallel programming model is discussed to show the effectiveness of our FCA-based solution.