Efficient Distributed Skyline Computation Using GPMRS and Mapreduce Frameworks for Large-Scale Spatial Data Mining

Abstract

As the size of the spatial data and its events are increasing in size, it is difficult to predict the skyline objects with ranking. Since, most of the traditional skyline processing models have static skyline optimization functions for local and global ranking process. In this work, a novel skyline computational models such as local, global and event grouping measures. In this framework, a Hadoop based skyline computational model is implemented using efficient density based clustering mechanism for post processing process. Experimental results prove that the proposed approach has better metrics than the conventional approaches on large skyline databases.