A Novel Filtered Based Grid partitioning multiple reducers skyline computation using Hadoop framework

Abstract

Due to the exponential growth of multi-dimensional skyline objects, the computational memory and efficiency of the traditional skyline measures also increases on large spatial datasets. Skyline query computation has attracted a major research problem recently, due to its exponential time and space complexities over imbalanced datasets. A large number of sequential skyline query processing models have been implemented to evaluate the spatial pattern discovery on limited datasets. It is practically expensive and time consuming process to predict various spatial patterns from a large spatial candidate sets. Another major limitation with the sequential spatial pattern mining models is that a large number of spatial candidate sets are generated with duplicate event sets. In the proposed model, a novel parallel skyline processing using MapReduce framework is implemented on large spatial uncertain datasets. In this model, a filtered based k-nearest neighbor approach is used to eliminate the sparsity or empty patterns using the hadoop framework. Experimental results proved that the proposed model has high computational efficiency in terms of time and candidate sets are concerned.